fix pep 8 tests

Author: laudavid

ot/lowrank.py

@@ -8,7 +8,7 @@
 
 
 import warnings
-from .utils import unif, list_to_array, get_lowrank_lazytensor
+from .utils import unif, get_lowrank_lazytensor
 from .backend import get_backend
 
 
@@ -287,4 +287,3 @@ def lowrank_sinkhorn(X_s, X_t, a=None, b=None, reg=0, rank="auto", alpha="auto",
         return Q, R, g, dict_log
 
     return Q, R, g
-

ot/solvers.py

@@ -1248,27 +1248,26 @@ def solve_sample(X_a, X_b, a=None, b=None, metric='sqeuclidean', reg=None, reg_t
             lazy_plan = log['lazy_plan']
             if not lazy0:  # store plan if not lazy
                 plan = lazy_plan[:]
-        
+
         elif method == "lowrank":
 
             if not metric.lower() in ['sqeuclidean']:
                 raise (NotImplementedError('Not implemented metric="{}"'.format(metric)))
-            
+
             if max_iter is None:
                 max_iter = 1000
             if tol is None:
                 tol = 1e-9
             if reg is None:
                 reg = 0
-            
+
             Q, R, g, log = lowrank_sinkhorn(X_a, X_b, reg=reg, a=a, b=b, numItermax=max_iter, stopThr=tol, log=True)
             value = log['value']
             value_linear = log['value_linear']
             lazy_plan = log['lazy_plan']
             if not lazy0:  # store plan if not lazy
                 plan = lazy_plan[:]
 
-
         elif method.startswith('geomloss'):  # Geomloss solver for entropi OT
 
             split_method = method.split('_')

ot/utils.py

@@ -1176,6 +1176,7 @@ def citation(self):
             }
         """
 
+
 class LazyTensor(object):
     """ A lazy tensor is a tensor that is not stored in memory. Instead, it is
     defined by a function that computes its values on the fly from slices.
@@ -1240,4 +1241,4 @@ def __getitem__(self, key):
         return self._getitem(*k, **self.kwargs)
 
     def __repr__(self):
-        return "LazyTensor(shape={},attributes=({}))".format(self.shape, ','.join(self.kwargs.keys()))
+        return "LazyTensor(shape={},attributes=({}))".format(self.shape, ','.join(self.kwargs.keys()))

test/test_solvers.py

@@ -30,14 +30,14 @@
     {'method': 'gaussian'},
     {'method': 'gaussian', 'reg': 1},
     {'method': 'factored', 'rank': 10},
-    {'method': 'lowrank', 'reg':0.1}
+    {'method': 'lowrank', 'reg': 0.1}
 ]
 
 lst_parameters_solve_sample_NotImplemented = [
     {'method': '1d', 'metric': 'any other one'},  # fail 1d on weird metrics
     {'method': 'gaussian', 'metric': 'euclidean'},  # fail gaussian on metric not euclidean
-    {'method': 'factored', 'metric': 'euclidean'}, # fail factored on metric not euclidean
-    {"method": 'lowrank', 'metric':'euclidean'}, # fail lowrank on metric not euclidean
+    {'method': 'factored', 'metric': 'euclidean'},  # fail factored on metric not euclidean
+    {"method": 'lowrank', 'metric': 'euclidean'},  # fail lowrank on metric not euclidean
     {'lazy': True},  # fail lazy for non regularized
     {'lazy': True, 'unbalanced': 1},  # fail lazy for non regularized unbalanced
     {'lazy': True, 'reg': 1, 'unbalanced': 1},  # fail lazy for unbalanced and regularized
@@ -415,7 +415,7 @@ def test_solve_sample_methods(nx, method_params):
     assert_allclose_sol(sol, solb)
 
     sol2 = ot.solve_sample(x, x, **method_params)
-    if method_params['method'] not in ['factored','lowrank']:
+    if method_params['method'] not in ['factored', 'lowrank']:
         np.testing.assert_allclose(sol2.value, 0)
 
 

test/test_utils.py

@@ -583,4 +583,3 @@ def test_lowrank_LazyTensor(nx):
     T = ot.utils.get_lowrank_lazytensor(X1, X2, diag_d, nx=nx)
 
     np.testing.assert_allclose(nx.to_numpy(T[:]), nx.to_numpy(T0))
-