diff options
| -rw-r--r-- | ot/da.py | 37 | ||||
| -rw-r--r-- | ot/gpu/da.py | 41 |
2 files changed, 21 insertions, 57 deletions
@@ -11,7 +11,7 @@ from .optim import cg from .optim import gcg -def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,unlabelledValue=-99,verbose=False,log=False): +def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -55,8 +55,6 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter Max number of iterations (inner sinkhorn solver) stopInnerThr : float, optional Stop threshold on error (inner sinkhorn solver) (>0) - unlabelledValue : int, optional - this value in array labels_a means this is an unlabelled example verbose : bool, optional Print information along iterations log : bool, optional @@ -84,41 +82,28 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter ot.optim.cg : General regularized OT """ - p=0.5 + p = 0.5 epsilon = 1e-3 - # init data - Nini = len(a) - Nfin = len(b) - indices_labels = [] classes = np.unique(labels_a) for c in classes: idxc, = np.where(labels_a == c) indices_labels.append(idxc) - W=np.zeros(M.shape) + W = np.zeros(M.shape) for cpt in range(numItermax): Mreg = M + eta*W - transp=sinkhorn(a,b,Mreg,reg,numItermax=numInnerItermax, stopThr=stopInnerThr) - # the transport has been computed. Check if classes are really separated - W = np.ones((Nini,Nfin)) - all_majs = [] - idx_unlabelled = -1 + transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, + stopThr=stopInnerThr) + # the transport has been computed. Check if classes are really + # separated + W = np.ones(M.shape) for (i, c) in enumerate(classes): - if c != unlabelledValue: - majs = np.sum(transp[indices_labels[i]], axis=0) - majs = p*((majs+epsilon)**(p-1)) - W[indices_labels[i]] = majs - all_majs.append(majs) - else: - idx_unlabelled = i - - # now we majorize the unlabelled (if there are any) by the min of - # the majorizations. do it only for unlabbled data - if idx_unlabelled != -1: - W[indices_labels[idx_unlabelled]] = np.min(all_majs, axis=0) + majs = np.sum(transp[indices_labels[i]], axis=0) + majs = p*((majs+epsilon)**(p-1)) + W[indices_labels[i]] = majs return transp diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 700a5e4..399e769 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -69,11 +69,9 @@ def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False): def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, - unlabelledValue=-99, verbose=False, log=False): + verbose=False, log=False): p = 0.5 epsilon = 1e-3 - - # init data Nfin = len(b) indices_labels = [] @@ -94,37 +92,18 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, # separated W_GPU.assign(1) W_GPU = W_GPU.transpose() - all_majs_GPU = [] - idx_unlabelled = -1 for (i, c) in enumerate(classes): - if c != unlabelledValue: - (_, nbRow) = indices_labels[i].shape - tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) - transp_GPU.transpose().select_columns(indices_labels[i], - tmpC_GPU) - majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) - cudamat.pow(majs_GPU, (p-1)) - majs_GPU.mult(p) - all_majs_GPU.append(majs_GPU) - - tmpC_GPU.assign(0) - tmpC_GPU.add_col_vec(majs_GPU) - W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) - else: - idx_unlabelled = i - - # now we majorize the unlabelled (if there are any) by the min of - # the majorizations. do it only for unlabbled data - if idx_unlabelled != -1: - all_majs = np.array([m_GPU.asarray() for m_GPU in all_majs_GPU]) - minMaj_GPU = (cudamat.CUDAMatrix(all_majs).min(axis=0) - .transpose()) - (_, nbRow) = indices_labels[idx_unlabelled].shape + (_, nbRow) = indices_labels[i].shape tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) + transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU) + majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) + cudamat.pow(majs_GPU, (p-1)) + majs_GPU.mult(p) + + tmpC_GPU.assign(0) + tmpC_GPU.add_col_vec(majs_GPU) + W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) - tmpC_GPU.add_col_vec(minMaj_GPU) - W_GPU.set_selected_columns(indices_labels[idx_unlabelled], - tmpC_GPU) W_GPU = W_GPU.transpose() return transp_GPU.asarray() |