diff options
| -rw-r--r-- | ot/da.py | 45 |
1 files changed, 22 insertions, 23 deletions
@@ -11,11 +11,7 @@ from .optim import cg from .optim import gcg -def indices(a, func): - return [i for (i, val) in enumerate(a) if func(val)] - - -def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): +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): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -46,7 +42,7 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter labels_a : np.ndarray (ns,) labels of samples in the source domain b : np.ndarray (nt,) - samples in the target domain + samples weights in the target domain M : np.ndarray (ns,nt) loss matrix reg : float @@ -59,6 +55,8 @@ 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 @@ -94,9 +92,9 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter Nfin = len(b) indices_labels = [] - idx_begin = np.min(labels_a) - for c in range(idx_begin,np.max(labels_a)+1): - idxc = indices(labels_a, lambda x: x==c) + classes = np.unique(labels_a) + for c in classes: + idxc, = np.where(labels_a == c) indices_labels.append(idxc) W=np.zeros(M.shape) @@ -106,20 +104,21 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter 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)) - for t in range(Nfin): - column = transp[:,t] - all_maj = [] - for c in range(idx_begin,np.max(labels_a)+1): - col_c = column[indices_labels[c-idx_begin]] - if c!=-1: - maj = p*((sum(col_c)+epsilon)**(p-1)) - W[indices_labels[c-idx_begin],t]=maj - all_maj.append(maj) - - # now we majorize the unlabelled by the min of the majorizations - # do it only for unlabbled data - if idx_begin==-1: - W[indices_labels[0],t]=np.min(all_maj) + all_majs = [] + idx_unlabelled = -1 + 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) return transp |