Changes to LP solver:

 - Allow to modify the maximal number of iterations
 - Display an error message in the python console if the solver encountered an issue

Author: toto6

ot/da.py

@@ -658,7 +658,7 @@ def __init__(self, metric='sqeuclidean'):
         self.metric = metric
         self.computed = False
 
-    def fit(self, xs, xt, ws=None, wt=None, norm=None):
+    def fit(self, xs, xt, ws=None, wt=None, norm=None, numItermax=10000):
         """Fit domain adaptation between samples is xs and xt
         (with optional weights)"""
         self.xs = xs
@@ -674,7 +674,7 @@ def fit(self, xs, xt, ws=None, wt=None, norm=None):
 
         self.M = dist(xs, xt, metric=self.metric)
         self.normalizeM(norm)
-        self.G = emd(ws, wt, self.M)
+        self.G = emd(ws, wt, self.M, numItermax)
         self.computed = True
 
     def interp(self, direction=1):

ot/lp/EMD.h

@@ -23,7 +23,12 @@
 using namespace lemon;
 typedef unsigned int node_id_type;
 
+enum ProblemType {
+    INFEASIBLE,
+    OPTIMAL,
+    UNBOUNDED
+};
 
-void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost);
+int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax);
 
 #endif

ot/lp/EMD_wrapper.cpp

@@ -15,11 +15,10 @@
 #include "EMD.h"
 
 
-void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost)  {
+int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax)  {
 // beware M and C anre strored in row major C style!!!
   int n, m, i,cur;
   double  max;
-  int max_iter=10000;
 
     typedef FullBipartiteDigraph Digraph;
   DIGRAPH_TYPEDEFS(FullBipartiteDigraph);
@@ -46,7 +45,7 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *
     std::vector<int> indI(n), indJ(m);
     std::vector<double> weights1(n), weights2(m);
     Digraph di(n, m);
-    NetworkSimplexSimple<Digraph,double,double, node_id_type> net(di, true, n+m, n*m,max_iter);
+    NetworkSimplexSimple<Digraph,double,double, node_id_type> net(di, true, n+m, n*m, numItermax);
 
     // Set supply and demand, don't account for 0 values (faster)
 
@@ -116,5 +115,5 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *
     };
 
 
-
+    return ret;
 }

ot/lp/__init__.py

@@ -15,7 +15,7 @@
 
 
 
-def emd(a, b, M):
+def emd(a, b, M, numItermax=10000):
     """Solves the Earth Movers distance problem and returns the OT matrix
 
 
@@ -40,6 +40,8 @@ def emd(a, b, M):
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
+    numItermax : int
+                 Maximum number of iterations made by the LP solver.
 
     Returns
     -------
@@ -84,9 +86,9 @@ def emd(a, b, M):
     if len(b) == 0:
         b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1]
 
-    return emd_c(a, b, M)
+    return emd_c(a, b, M, numItermax)
 
-def emd2(a, b, M,processes=multiprocessing.cpu_count()):
+def emd2(a, b, M, numItermax=10000, processes=multiprocessing.cpu_count()):
     """Solves the Earth Movers distance problem and returns the loss 
 
     .. math::
@@ -110,6 +112,8 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()):
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
+    numItermax : int
+                 Maximum number of iterations made by the LP solver.
 
     Returns
     -------
@@ -155,12 +159,12 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()):
         b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1]
 
     if len(b.shape)==1:
-        return emd2_c(a, b, M)
+        return emd2_c(a, b, M, numItermax)
     else:
         nb=b.shape[1]
-        #res=[emd2_c(a,b[:,i].copy(),M) for i in range(nb)]
+        #res=[emd2_c(a,b[:,i].copy(),M, numItermax) for i in range(nb)]
         def f(b):
-            return emd2_c(a,b,M)
+            return emd2_c(a,b,M, numItermax)
         res= parmap(f, [b[:,i] for i in range(nb)],processes)
         return np.array(res)
 

ot/lp/emd_wrap.pyx

@@ -15,13 +15,14 @@ cimport cython
 
 
 cdef extern from "EMD.h":
-    void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost)
+    int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax)
+    cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED
 
 
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"]  b,np.ndarray[double, ndim=2, mode="c"]  M):
+def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"]  b,np.ndarray[double, ndim=2, mode="c"]  M, int numItermax):
     """
         Solves the Earth Movers distance problem and returns the optimal transport matrix
         
@@ -48,6 +49,8 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod
         target histogram
     M : (ns,nt) ndarray, float64
         loss matrix        
+    numItermax : int
+                 Maximum number of iterations made by the LP solver.
   
     
     Returns
@@ -69,13 +72,18 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod
         b=np.ones((n2,))/n2
 
     # calling the function
-    EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost)
+    cdef int resultSolver = EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost, numItermax)
+    if resultSolver != OPTIMAL:
+        if resultSolver == INFEASIBLE:
+            print("Problem infeasible. Try to inscrease numItermax.")
+        elif resultSolver == UNBOUNDED:
+            print("Problem unbounded")
 
     return G
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"]  b,np.ndarray[double, ndim=2, mode="c"]  M):
+def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"]  b,np.ndarray[double, ndim=2, mode="c"]  M, int numItermax):
     """
         Solves the Earth Movers distance problem and returns the optimal transport loss
         
@@ -102,6 +110,8 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo
         target histogram
     M : (ns,nt) ndarray, float64
         loss matrix        
+    numItermax : int
+                 Maximum number of iterations made by the LP solver.
   
     
     Returns
@@ -123,7 +133,12 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo
         b=np.ones((n2,))/n2
 
     # calling the function
-    EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost)
+    cdef int resultSolver = EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost, numItermax)
+    if resultSolver != OPTIMAL:
+        if resultSolver == INFEASIBLE:
+            print("Problem infeasible. Try to inscrease numItermax.")
+        elif resultSolver == UNBOUNDED:
+            print("Problem unbounded")
 
     cost=0
     for i in range(n1):