changes forgotten coments

Author: tvayer

ot/gromov.py

@@ -17,7 +17,7 @@
 
 
 from .bregman import sinkhorn
-from .utils import dist
+from .utils import dist, UndefinedParameter
 from .optim import cg
 
 
@@ -1011,9 +1011,6 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_
         International Conference on Machine Learning (ICML). 2019.
     """
 
-    class UndefinedParameter(Exception):
-        pass
-
     S = len(Cs)
     d = Ys[0].shape[1]  # dimension on the node features
     if p is None:
@@ -1049,10 +1046,7 @@ class UndefinedParameter(Exception):
 
     T = [np.outer(p, q) for q in ps]
 
-    # X is N,d
-    # Ys is ns,d
-    Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))]
-    # Ms is N,ns
+    Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))]  # Ms is N,ns
 
     cpt = 0
     err_feature = 1
@@ -1072,27 +1066,13 @@ class UndefinedParameter(Exception):
             Ys_temp = [y.T for y in Ys]
             X = update_feature_matrix(lambdas, Ys_temp, T, p).T
 
-        # X must be N,d
-        # Ys must be ns,d
         Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))]
 
         if not fixed_structure:
             if loss_fun == 'square_loss':
-                # T must be ns,N
-                # Cs must be ns,ns
-                # p must be N,1
                 T_temp = [t.T for t in T]
                 C = update_sructure_matrix(p, lambdas, T_temp, Cs)
 
-        # Ys must be d,ns
-        # Ts must be N,ns
-        # p must be N,1
-        # Ms is N,ns
-        # C is N,N
-        # Cs is ns,ns
-        # p is N,1
-        # ps is ns,1
-
         T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha, numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)]
 
         # T is N,ns
@@ -1115,7 +1095,7 @@ class UndefinedParameter(Exception):
     if log:
         log_['T'] = T  # from target to Ys
         log_['p'] = p
-        log_['Ms'] = Ms  # Ms are N,ns
+        log_['Ms'] = Ms
 
     if log:
         return X, C, log_

ot/optim.py

@@ -73,8 +73,8 @@ def phi(alpha1):
     return alpha, fc[0], phi1
 
 
-def do_linesearch(cost, G, deltaG, Mi, f_val,
-                  armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None):
+def solve_linesearch(cost, G, deltaG, Mi, f_val,
+                     armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None):
     """
     Solve the linesearch in the FW iterations
     Parameters
@@ -93,17 +93,17 @@ def do_linesearch(cost, G, deltaG, Mi, f_val,
             If True the steps of the line-search is found via an armijo research. Else closed form is used.
             If there is convergence issues use False.
     C1 : ndarray (ns,ns), optional
-        Structure matrix in the source domain. Only used when armijo=False
+        Structure matrix in the source domain. Only used and necessary when armijo=False
     C2 : ndarray (nt,nt), optional
-        Structure matrix in the target domain. Only used when armijo=False
+        Structure matrix in the target domain. Only used and necessary when armijo=False
     reg : float, optional
-          Regularization parameter. Only used when armijo=False
+          Regularization parameter. Only used and necessary when armijo=False
     Gc : ndarray (ns,nt)
-        Optimal map found by linearization in the FW algorithm. Only used when armijo=False
+        Optimal map found by linearization in the FW algorithm. Only used and necessary when armijo=False
     constC : ndarray (ns,nt)
-             Constant for the gromov cost. See [24]. Only used when armijo=False
+             Constant for the gromov cost. See [24]. Only used and necessary when armijo=False
     M : ndarray (ns,nt), optional
-        Cost matrix between the features. Only used when armijo=False
+        Cost matrix between the features. Only used and necessary when armijo=False
     Returns
     -------
     alpha : float
@@ -128,7 +128,7 @@ def do_linesearch(cost, G, deltaG, Mi, f_val,
         b = np.sum((M + reg * constC) * deltaG) - 2 * reg * (np.sum(dot12 * G) + np.sum(np.dot(C1, G).dot(C2) * deltaG))
         c = cost(G)
 
-        alpha = solve_1d_linesearch_quad_funct(a, b, c)
+        alpha = solve_1d_linesearch_quad(a, b, c)
         fc = None
         f_val = cost(G + alpha * deltaG)
 
@@ -181,7 +181,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
         Print information along iterations
     log : bool, optional
         record log if True
-    kwargs : dict
+    **kwargs : dict
              Parameters for linesearch
 
     Returns
@@ -244,7 +244,7 @@ def cost(G):
         deltaG = Gc - G
 
         # line search
-        alpha, fc, f_val = do_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs)
+        alpha, fc, f_val = solve_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs)
 
         G = G + alpha * deltaG
 
@@ -254,7 +254,7 @@ def cost(G):
 
         abs_delta_fval = abs(f_val - old_fval)
         relative_delta_fval = abs_delta_fval / abs(f_val)
-        if relative_delta_fval < stopThr and abs_delta_fval < stopThr2:
+        if relative_delta_fval < stopThr or abs_delta_fval < stopThr2:
             loop = 0
 
         if log:
@@ -395,7 +395,7 @@ def cost(G):
         abs_delta_fval = abs(f_val - old_fval)
         relative_delta_fval = abs_delta_fval / abs(f_val)
 
-        if relative_delta_fval < stopThr and abs_delta_fval < stopThr2:
+        if relative_delta_fval < stopThr or abs_delta_fval < stopThr2:
             loop = 0
 
         if log:
@@ -413,11 +413,11 @@ def cost(G):
         return G
 
 
-def solve_1d_linesearch_quad_funct(a, b, c):
+def solve_1d_linesearch_quad(a, b, c):
     """
-    Solve on 0,1 the following problem:
+    For any convex or non-convex 1d quadratic function f, solve on [0,1] the following problem:
     .. math::
-        \min f(x)=a*x^{2}+b*x+c
+        \argmin f(x)=a*x^{2}+b*x+c
 
     Parameters
     ----------

ot/utils.py

@@ -487,3 +487,11 @@ def set_params(self, **params):
                                      (key, self.__class__.__name__))
                 setattr(self, key, value)
         return self
+
+
+class UndefinedParameter(Exception):
+    """
+    Aim at raising an Exception when a undefined parameter is called
+
+    """
+    pass

test/test_optim.py

@@ -68,6 +68,6 @@ def df(G):
 
 
 def test_solve_1d_linesearch_quad_funct():
-    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(1, -1, 0), 0.5)
-    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1, 5, 0), 0)
-    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1, 0.5, 0), 1)
+    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(1, -1, 0), 0.5)
+    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 5, 0), 0)
+    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 0.5, 0), 1)