diff options
Diffstat (limited to 'ot/da.py')
| -rw-r--r-- | ot/da.py | 407 |
1 files changed, 66 insertions, 341 deletions
@@ -15,7 +15,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel, cost_normalization -from .utils import check_params, deprecated, BaseEstimator +from .utils import check_params, BaseEstimator from .optim import cg from .optim import gcg @@ -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` @@ -740,288 +743,6 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -@deprecated("The class OTDA is deprecated in 0.3.1 and will be " - "removed in 0.5" - "\n\tfor standard transport use class EMDTransport instead.") -class OTDA(object): - - """Class for domain adaptation with optimal transport as proposed in [5] - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions on - Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - - """ - - def __init__(self, metric='sqeuclidean', norm=None): - """ Class initialization""" - self.xs = 0 - self.xt = 0 - self.G = 0 - self.metric = metric - self.norm = norm - self.computed = False - - def fit(self, xs, xt, ws=None, wt=None, max_iter=100000): - """Fit domain adaptation between samples is xs and xt - (with optional weights)""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = emd(ws, wt, self.M, max_iter) - self.computed = True - - def interp(self, direction=1): - """Barycentric interpolation for the source (1) or target (-1) samples - - This Barycentric interpolation solves for each source (resp target) - sample xs (resp xt) the following optimization problem: - - .. math:: - arg\min_x \sum_i \gamma_{k,i} c(x,x_i^t) - - where k is the index of the sample in xs - - For the moment only squared euclidean distance is provided but more - metric could be used in the future. - - """ - if direction > 0: # >0 then source to target - G = self.G - w = self.ws.reshape((self.xs.shape[0], 1)) - x = self.xt - else: - G = self.G.T - w = self.wt.reshape((self.xt.shape[0], 1)) - x = self.xs - - if self.computed: - if self.metric == 'sqeuclidean': - return np.dot(G / w, x) # weighted mean - else: - print( - "Warning, metric not handled yet, using weighted average") - return np.dot(G / w, x) # weighted mean - return None - else: - print("Warning, model not fitted yet, returning None") - return None - - def predict(self, x, direction=1): - """ Out of sample mapping using the formulation from [6] - - For each sample x to map, it finds the nearest source sample xs and - map the samle x to the position xst+(x-xs) wher xst is the barycentric - interpolation of source sample xs. - - References - ---------- - - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging - Sciences, 7(3), 1853-1882. - - """ - if direction > 0: # >0 then source to target - xf = self.xt - x0 = self.xs - else: - xf = self.xs - x0 = self.xt - - D0 = dist(x, x0) # dist netween new samples an source - idx = np.argmin(D0, 1) # closest one - xf = self.interp(direction) # interp the source samples - # aply the delta to the interpolation - return xf[idx, :] + x - x0[idx, :] - - -@deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornTransport instead.") -class OTDA_sinkhorn(OTDA): - - """Class for domain adaptation with optimal transport with entropic - regularization - - - """ - - def fit(self, xs, xt, reg=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights)""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_lpl1 is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornLpl1Transport instead.") -class OTDA_lpl1(OTDA): - - """Class for domain adaptation with optimal transport with entropic and - group regularization""" - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit - parameters""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_l1L2 is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornL1l2Transport instead.") -class OTDA_l1l2(OTDA): - - """Class for domain adaptation with optimal transport with entropic - and group lasso regularization""" - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit - parameters""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_mapping_linear is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class MappingTransport instead.") -class OTDA_mapping_linear(OTDA): - - """Class for optimal transport with joint linear mapping estimation as in - [8] - """ - - def __init__(self): - """ Class initialization""" - - self.xs = 0 - self.xt = 0 - self.G = 0 - self.L = 0 - self.bias = False - self.computed = False - self.metric = 'sqeuclidean' - - def fit(self, xs, xt, mu=1, eta=1, bias=False, **kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional - weights)""" - self.xs = xs - self.xt = xt - self.bias = bias - - self.ws = unif(xs.shape[0]) - self.wt = unif(xt.shape[0]) - - self.G, self.L = joint_OT_mapping_linear( - xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) - self.computed = True - - def mapping(self): - return lambda x: self.predict(x) - - def predict(self, x): - """ Out of sample mapping estimated during the call to fit""" - if self.computed: - if self.bias: - x = np.hstack((x, np.ones((x.shape[0], 1)))) - return x.dot(self.L) # aply the delta to the interpolation - else: - print("Warning, model not fitted yet, returning None") - return None - - -@deprecated("The class OTDA_mapping_kernel is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class MappingTransport instead.") -class OTDA_mapping_kernel(OTDA_mapping_linear): - - """Class for optimal transport with joint nonlinear mapping - estimation as in [8]""" - - def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', - sigma=1, **kwargs): - """ Fit domain adaptation between samples is xs and xt """ - self.xs = xs - self.xt = xt - self.bias = bias - - self.ws = unif(xs.shape[0]) - self.wt = unif(xt.shape[0]) - self.kernel = kerneltype - self.sigma = sigma - self.kwargs = kwargs - - self.G, self.L = joint_OT_mapping_kernel( - xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) - self.computed = True - - def predict(self, x): - """ Out of sample mapping estimated during the call to fit""" - - if self.computed: - K = kernel( - x, self.xs, method=self.kernel, sigma=self.sigma, - **self.kwargs) - if self.bias: - K = np.hstack((K, np.ones((x.shape[0], 1)))) - return K.dot(self.L) - else: - print("Warning, model not fitted yet, returning None") - return None - - def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X @@ -1466,25 +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 ---------- @@ -1569,22 +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 @@ -1669,28 +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 ---------- @@ -1786,27 +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 @@ -1928,10 +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 ---------- |