Merge pull request #25 from aje/master

Add iter_max to lp solver and fixes #24

Author: rflamary

ot/da.py

@@ -13,7 +13,7 @@
 
 from .bregman import sinkhorn
 from .lp import emd
-from .utils import unif, dist, kernel
+from .utils import unif, dist, kernel, cost_normalization
 from .utils import check_params, deprecated, BaseEstimator
 from .optim import cg
 from .optim import gcg
@@ -650,15 +650,16 @@ class OTDA(object):
 
     """
 
-    def __init__(self, metric='sqeuclidean'):
+    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, norm=None):
+    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
@@ -673,8 +674,8 @@ def fit(self, xs, xt, ws=None, wt=None, norm=None):
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
-        self.G = emd(ws, wt, self.M)
+        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):
@@ -741,26 +742,6 @@ def predict(self, x, direction=1):
         # aply the delta to the interpolation
         return xf[idx, :] + x - x0[idx, :]
 
-    def normalizeM(self, norm):
-        """ Apply normalization to the loss matrix
-
-
-        Parameters
-        ----------
-        norm : str
-            type of normalization from 'median','max','log','loglog'
-
-        """
-
-        if norm == "median":
-            self.M /= float(np.median(self.M))
-        elif norm == "max":
-            self.M /= float(np.max(self.M))
-        elif norm == "log":
-            self.M = np.log(1 + self.M)
-        elif norm == "loglog":
-            self.M = np.log(1 + np.log(1 + self.M))
-
 
 @deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be"
             " removed in 0.5 \nUse class SinkhornTransport instead.")
@@ -772,7 +753,7 @@ class OTDA_sinkhorn(OTDA):
 
     """
 
-    def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs):
+    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
@@ -787,7 +768,7 @@ def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs):
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
+        self.M = cost_normalization(self.M, self.norm)
         self.G = sinkhorn(ws, wt, self.M, reg, **kwargs)
         self.computed = True
 
@@ -799,8 +780,7 @@ 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, norm=None,
-            **kwargs):
+    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"""
@@ -816,7 +796,7 @@ def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
+        self.M = cost_normalization(self.M, self.norm)
         self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs)
         self.computed = True
 
@@ -828,8 +808,7 @@ 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, norm=None,
-            **kwargs):
+    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"""
@@ -845,7 +824,7 @@ def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
+        self.M = cost_normalization(self.M, self.norm)
         self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs)
         self.computed = True
 
@@ -1001,6 +980,7 @@ def fit(self, Xs=None, ys=None, Xt=None, yt=None):
 
             # pairwise distance
             self.cost_ = dist(Xs, Xt, metric=self.metric)
+            self.cost_ = cost_normalization(self.cost_, self.norm)
 
             if (ys is not None) and (yt is not None):
 
@@ -1202,6 +1182,9 @@ class SinkhornTransport(BaseTransport):
         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)
@@ -1231,7 +1214,7 @@ class SinkhornTransport(BaseTransport):
 
     def __init__(self, reg_e=1., max_iter=1000,
                  tol=10e-9, verbose=False, log=False,
-                 metric="sqeuclidean",
+                 metric="sqeuclidean", norm=None,
                  distribution_estimation=distribution_estimation_uniform,
                  out_of_sample_map='ferradans', limit_max=np.infty):
 
@@ -1241,6 +1224,7 @@ def __init__(self, reg_e=1., max_iter=1000,
         self.verbose = verbose
         self.log = log
         self.metric = metric
+        self.norm = norm
         self.limit_max = limit_max
         self.distribution_estimation = distribution_estimation
         self.out_of_sample_map = out_of_sample_map
@@ -1296,6 +1280,9 @@ class EMDTransport(BaseTransport):
         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)
@@ -1306,6 +1293,9 @@ class EMDTransport(BaseTransport):
         Controls the semi supervised mode. Transport between labeled source
         and target samples of different classes will exhibit an infinite cost
         (10 times the maximum value of the cost matrix)
+    max_iter : int, optional (default=100000)
+        The maximum number of iterations before stopping the optimization
+        algorithm if it has not converged.
 
     Attributes
     ----------
@@ -1319,14 +1309,17 @@ class EMDTransport(BaseTransport):
            on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
     """
 
-    def __init__(self, metric="sqeuclidean",
+    def __init__(self, metric="sqeuclidean", norm=None,
                  distribution_estimation=distribution_estimation_uniform,
-                 out_of_sample_map='ferradans', limit_max=10):
+                 out_of_sample_map='ferradans', limit_max=10,
+                 max_iter=100000):
 
         self.metric = metric
+        self.norm = norm
         self.limit_max = limit_max
         self.distribution_estimation = distribution_estimation
         self.out_of_sample_map = out_of_sample_map
+        self.max_iter = max_iter
 
     def fit(self, Xs, ys=None, Xt=None, yt=None):
         """Build a coupling matrix from source and target sets of samples
@@ -1353,7 +1346,7 @@ def fit(self, Xs, ys=None, Xt=None, yt=None):
 
         # coupling estimation
         self.coupling_ = emd(
-            a=self.mu_s, b=self.mu_t, M=self.cost_,
+            a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter
         )
 
         return self
@@ -1376,6 +1369,9 @@ class SinkhornLpl1Transport(BaseTransport):
         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)
@@ -1410,7 +1406,7 @@ class SinkhornLpl1Transport(BaseTransport):
     def __init__(self, reg_e=1., reg_cl=0.1,
                  max_iter=10, max_inner_iter=200,
                  tol=10e-9, verbose=False,
-                 metric="sqeuclidean",
+                 metric="sqeuclidean", norm=None,
                  distribution_estimation=distribution_estimation_uniform,
                  out_of_sample_map='ferradans', limit_max=np.infty):
 
@@ -1421,6 +1417,7 @@ def __init__(self, reg_e=1., reg_cl=0.1,
         self.tol = tol
         self.verbose = verbose
         self.metric = metric
+        self.norm = norm
         self.distribution_estimation = distribution_estimation
         self.out_of_sample_map = out_of_sample_map
         self.limit_max = limit_max
@@ -1477,6 +1474,9 @@ class SinkhornL1l2Transport(BaseTransport):
         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)
@@ -1516,7 +1516,7 @@ class SinkhornL1l2Transport(BaseTransport):
     def __init__(self, reg_e=1., reg_cl=0.1,
                  max_iter=10, max_inner_iter=200,
                  tol=10e-9, verbose=False, log=False,
-                 metric="sqeuclidean",
+                 metric="sqeuclidean", norm=None,
                  distribution_estimation=distribution_estimation_uniform,
                  out_of_sample_map='ferradans', limit_max=10):
 
@@ -1528,6 +1528,7 @@ def __init__(self, reg_e=1., reg_cl=0.1,
         self.verbose = verbose
         self.log = log
         self.metric = metric
+        self.norm = norm
         self.distribution_estimation = distribution_estimation
         self.out_of_sample_map = out_of_sample_map
         self.limit_max = limit_max
@@ -1588,6 +1589,9 @@ class MappingTransport(BaseEstimator):
         Estimate linear mapping with constant bias
     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.
     kernel : string, optional (default="linear")
         The kernel to use either linear or gaussian
     sigma : float, optional (default=1)
@@ -1627,11 +1631,12 @@ class MappingTransport(BaseEstimator):
     """
 
     def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean",
-                 kernel="linear", sigma=1, max_iter=100, tol=1e-5,
+                 norm=None, kernel="linear", sigma=1, max_iter=100, tol=1e-5,
                  max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False,
                  verbose2=False):
 
         self.metric = metric
+        self.norm = norm
         self.mu = mu
         self.eta = eta
         self.bias = bias

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 max_iter);
 
 #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 max_iter)  {
 // 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, max_iter);
 
     // 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;
 }