diff options
Diffstat (limited to 'ot/da.py')
-rw-r--r-- | ot/da.py | 123 |
1 files changed, 65 insertions, 58 deletions
@@ -473,22 +473,24 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', Weight for the linear OT loss (>0) eta : float, optional Regularization term for the linear mapping L (>0) - bias : bool,optional - Estimate linear mapping with constant bias kerneltype : str,optional kernel used by calling function ot.utils.kernel (gaussian by default) sigma : float, optional Gaussian kernel bandwidth. + bias : bool,optional + Estimate linear mapping with constant bias + verbose : bool, optional + Print information along iterations + verbose2 : bool, optional + Print information along iterations numItermax : int, optional Max number of BCD iterations - stopThr : float, optional - Stop threshold on relative loss decrease (>0) numInnerItermax : int, optional Max number of iterations (inner CG solver) stopInnerThr : float, optional Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional - Print information along iterations + stopThr : float, optional + Stop threshold on relative loss decrease (>0) log : bool, optional record log if True @@ -643,7 +645,8 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` - and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark + 2.29 in [15]. The linear operator from source to target :math:`M` @@ -1184,25 +1187,25 @@ class SinkhornTransport(BaseTransport): algorithm if no it has not converged tol : float, optional (default=10e-9) The precision required to stop the optimization algorithm. - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : int, optional (default=False) + Controls the logs of the optimization algorithm metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem norm : string, optional (default=None) If given, normalize the ground metric to avoid numerical errors that can occur with large metric values. - distribution : string, optional (default="uniform") + distribution_estimation : callable, optional (defaults to the uniform) The kind of distribution estimation to employ - verbose : int, optional (default=0) - Controls the verbosity of the optimization algorithm - log : int, optional (default=0) - Controls the logs of the optimization algorithm + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost + and target samples of different classes will exhibit an cost defined + by this variable Attributes ---------- @@ -1287,22 +1290,19 @@ class EMDTransport(BaseTransport): Parameters ---------- - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem norm : string, optional (default=None) If given, normalize the ground metric to avoid numerical errors that can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ - verbose : int, optional (default=0) - Controls the verbosity of the optimization algorithm - log : int, optional (default=0) + log : int, optional (default=False) Controls the logs of the optimization algorithm + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost @@ -1387,28 +1387,32 @@ class SinkhornLpl1Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ max_iter : int, float, optional (default=10) The minimum number of iteration before stopping the optimization algorithm if no it has not converged max_inner_iter : int, float, optional (default=200) The number of iteration in the inner loop - verbose : int, optional (default=0) + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) Controls the verbosity of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost + and target samples of different classes will exhibit a cost defined by + limit_max. Attributes ---------- @@ -1504,27 +1508,28 @@ class SinkhornL1l2Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ max_iter : int, float, optional (default=10) The minimum number of iteration before stopping the optimization algorithm if no it has not converged max_inner_iter : int, float, optional (default=200) The number of iteration in the inner loop - verbose : int, optional (default=0) + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) Controls the verbosity of the optimization algorithm - log : int, optional (default=0) + log : bool, optional (default=False) Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost @@ -1646,10 +1651,12 @@ class MappingTransport(BaseEstimator): Max number of iterations (inner CG solver) inner_tol : float, optional (default=1e-6) Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional (default=False) - Print information along iterations log : bool, optional (default=False) record log if True + verbose : bool, optional (default=False) + Print information along iterations + verbose2 : bool, optional (default=False) + Print information along iterations Attributes ---------- |