Changes:

 - Rename numItermax to max_iter
 - Default value to 100000 instead of 10000
 - Add max_iter to class SinkhornTransport(BaseTransport)
 - Add norm to all BaseTransport

1 files changed, 55 insertions, 9 deletions

diff --git a/ot/da.py b/ot/da.py
index 0dfd02f..5871aba 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -658,7 +658,7 @@ class OTDA(object):
         self.metric = metric
         self.computed = False
 
-    def fit(self, xs, xt, ws=None, wt=None, norm=None, numItermax=10000):
+    def fit(self, xs, xt, ws=None, wt=None, norm=None, max_iter=100000):
         """Fit domain adaptation between samples is xs and xt
         (with optional weights)"""
         self.xs = xs
@@ -674,7 +674,7 @@ class OTDA(object):
 
         self.M = dist(xs, xt, metric=self.metric)
         self.normalizeM(norm)
-        self.G = emd(ws, wt, self.M, numItermax)
+        self.G = emd(ws, wt, self.M, max_iter)
         self.computed = True
 
     def interp(self, direction=1):
@@ -1001,6 +1001,7 @@ class BaseTransport(BaseEstimator):
 
             # pairwise distance
             self.cost_ = dist(Xs, Xt, metric=self.metric)
+            self.normalizeCost_(self.norm)
 
             if (ys is not None) and (yt is not None):
 
@@ -1182,6 +1183,26 @@ class BaseTransport(BaseEstimator):
 
             return transp_Xt
 
+    def normalizeCost_(self, norm):
+        """ Apply normalization to the loss matrix
+
+
+        Parameters
+        ----------
+        norm : str
+            type of normalization from 'median','max','log','loglog'
+
+        """
+
+        if norm == "median":
+            self.cost_ /= float(np.median(self.cost_))
+        elif norm == "max":
+            self.cost_ /= float(np.max(self.cost_))
+        elif norm == "log":
+            self.cost_ = np.log(1 + self.cost_)
+        elif norm == "loglog":
+            self.cost_ = np.log(1 + np.log(1 + self.cost_))
+
 
 class SinkhornTransport(BaseTransport):
     """Domain Adapatation OT method based on Sinkhorn Algorithm
@@ -1202,6 +1223,9 @@ 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 +1255,7 @@ 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 +1265,7 @@ class SinkhornTransport(BaseTransport):
         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 +1321,9 @@ 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 +1334,9 @@ 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 +1350,17 @@ 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 +1387,7 @@ class EMDTransport(BaseTransport):
 
         # 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_, max_iter=self.max_iter
         )
 
         return self
@@ -1376,6 +1410,9 @@ 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 +1447,7 @@ 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 +1458,7 @@ class SinkhornLpl1Transport(BaseTransport):
         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 +1515,9 @@ 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 +1557,7 @@ 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 +1569,7 @@ class SinkhornL1l2Transport(BaseTransport):
         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 +1630,9 @@ 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 +1672,12 @@ 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