performance improvement sinkhorn lpl1

 - instead of updating individually for each target examples, update for all target examples at once using numpy functions. This allows for a faster computation (for me, divided by 4 on 3000*100 random matricies and random labels in [0,1]).

 - if I understoud correctly, a value of -1 in the array labels_a meant that we didn't have a label for this example. But in machine learning, we often encounter the binary case where we say we have the positive class (+1) and negative class (-1); thus with a dataset like this, the algorithm wouldn't work as expected. I replaced the default value for 'no label' to '-99' instead of '-1', and I added a parameter to modify it.

Author: Leo gautheron

ot/da.py

@@ -11,11 +11,7 @@
 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