Returned to old variable name to follow repo convention

Author: arolet

ot/da.py

@@ -1370,7 +1370,7 @@ class label
 
         # coupling estimation
         self.coupling_ = emd(
-            a=self.mu_s, b=self.mu_t, M=self.cost_, max_iter=self.max_iter
+            a=self.mu_s, b=self.mu_t, M=self.cost_, num_iter_max=self.max_iter
         )
 
         return self

ot/lp/__init__.py

@@ -16,7 +16,7 @@
 from ..utils import parmap
 
 
-def emd(a, b, M, max_iter=100000, log=False):
+def emd(a, b, M, num_iter_max=100000, log=False):
     """Solves the Earth Movers distance problem and returns the OT matrix
 
 
@@ -41,7 +41,7 @@ def emd(a, b, M, max_iter=100000, log=False):
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
-    max_iter : int, optional (default=100000)
+    num_iter_max : int, optional (default=100000)
         The maximum number of iterations before stopping the optimization
         algorithm if it has not converged.
     log: boolean, optional (default=False)
@@ -94,7 +94,7 @@ def emd(a, b, M, max_iter=100000, log=False):
     if len(b) == 0:
         b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
 
-    G, cost, u, v, result_code = emd_c(a, b, M, max_iter)
+    G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max)
     result_code_string = check_result(result_code)
     if log:
         log = {}
@@ -107,7 +107,7 @@ def emd(a, b, M, max_iter=100000, log=False):
     return G
 
 
-def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=False):
+def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False):
     """Solves the Earth Movers distance problem and returns the loss
 
     .. math::
@@ -183,7 +183,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=Fa
 
     if log:
         def f(b):
-            G, cost, u, v, resultCode = emd_c(a, b, M, max_iter)
+            G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max)
             result_code_string = check_result(resultCode)
             log = {}
             log['G'] = G
@@ -194,7 +194,7 @@ def f(b):
             return [cost, log]
     else:
         def f(b):
-            G, cost, u, v, result_code = emd_c(a, b, M, max_iter)
+            G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max)
             check_result(result_code)
             return cost
 

ot/lp/emd_wrap.pyx

@@ -29,7 +29,7 @@ def check_result(result_code):
     elif result_code == UNBOUNDED:
         message = "Problem unbounded"
     elif result_code == MAX_ITER_REACHED:
-        message = "max_iter reached before optimality. Try to increase max_iter."
+        message = "num_iter_max reached before optimality. Try to increase num_iter_max."
     warnings.warn(message)
     return message
 

test/test_ot.py

@@ -140,8 +140,8 @@ def test_warnings():
     with warnings.catch_warnings(record=True) as w:
         warnings.simplefilter("always")
         print('Computing {} EMD '.format(1))
-        ot.emd(a, b, M, max_iter=1)
-        assert "max_iter" in str(w[-1].message)
+        ot.emd(a, b, M, num_iter_max=1)
+        assert "num_iter_max" in str(w[-1].message)
         assert len(w) == 1
         a[0] = 100
         print('Computing {} EMD '.format(2))