docstrings and naming

Author: Nicolas Courty

examples/plot_gromov.py

@@ -26,7 +26,7 @@
 For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces.
 """
 
-n = 30  # nb samples
+n_samples = 30  # nb samples
 
 mu_s = np.array([0, 0])
 cov_s = np.array([[1, 0], [0, 1]])
@@ -35,9 +35,9 @@
 cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 
 
-xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
+xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
 P = sp.linalg.sqrtm(cov_t)
-xt = np.random.randn(n, 3).dot(P) + mu_t
+xt = np.random.randn(n_samples, 3).dot(P) + mu_t
 
 
 """
@@ -75,8 +75,8 @@
 =============================================
 """
 
-p = ot.unif(n)
-q = ot.unif(n)
+p = ot.unif(n_samples)
+q = ot.unif(n_samples)
 
 gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)
 gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)

examples/plot_gromov_barycenter.py

@@ -91,12 +91,12 @@ def im2mat(I):
     return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
 
 
-carre = spi.imread('../data/carre.png').astype(np.float64) / 256
-rond = spi.imread('../data/rond.png').astype(np.float64) / 256
+square = spi.imread('../data/carre.png').astype(np.float64) / 256
+circle = spi.imread('../data/rond.png').astype(np.float64) / 256
 triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256
-fleche = spi.imread('../data/coeur.png').astype(np.float64) / 256
+arrow = spi.imread('../data/coeur.png').astype(np.float64) / 256
 
-shapes = [carre, rond, triangle, fleche]
+shapes = [square, circle, triangle, arrow]
 
 S = 4
 xs = [[] for i in range(S)]
@@ -118,36 +118,36 @@ def im2mat(I):
 The four distributions are constructed from 4 simple images
 """
 ns = [len(xs[s]) for s in range(S)]
-N = 30
+n_samples = 30
 
 """Compute all distances matrices for the four shapes"""
 Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]
 Cs = [cs / cs.max() for cs in Cs]
 
 ps = [ot.unif(ns[s]) for s in range(S)]
-p = ot.unif(N)
+p = ot.unif(n_samples)
 
 
 lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]
 
 Ct01 = [0 for i in range(2)]
 for i in range(2):
-    Ct01[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[1]], [
+    Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [
                                            ps[0], ps[1]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)
 
 Ct02 = [0 for i in range(2)]
 for i in range(2):
-    Ct02[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[2]], [
+    Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [
                                            ps[0], ps[2]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)
 
 Ct13 = [0 for i in range(2)]
 for i in range(2):
-    Ct13[i] = ot.gromov.gromov_barycenters(N, [Cs[1], Cs[3]], [
+    Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [
                                            ps[1], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)
 
 Ct23 = [0 for i in range(2)]
 for i in range(2):
-    Ct23[i] = ot.gromov.gromov_barycenters(N, [Cs[2], Cs[3]], [
+    Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [
                                            ps[2], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3)
 
 """

ot/gromov.py

@@ -208,7 +208,7 @@ def update_kl_loss(p, lambdas, T, Cs):
     return(np.exp(np.divide(tmpsum, ppt)))
 
 
-def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
+def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):
     """
     Returns the gromov-wasserstein coupling between the two measured similarity matrices
 
@@ -248,7 +248,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr
     loss_fun :  loss function used for the solver either 'square_loss' or 'kl_loss'
     epsilon : float
         Regularization term >0
-    numItermax : int, optional
+    max_iter : int, optional
         Max number of iterations
     stopThr : float, optional
         Stop threshold on error (>0)
@@ -274,7 +274,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr
     cpt = 0
     err = 1
 
-    while (err > stopThr and cpt < numItermax):
+    while (err > stopThr and cpt < max_iter):
 
         Tprev = T
 
@@ -307,7 +307,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr
         return T
 
 
-def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
+def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):
     """
     Returns the gromov-wasserstein discrepancy between the two measured similarity matrices
 
@@ -362,10 +362,10 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh
 
     if log:
         gw, logv = gromov_wasserstein(
-            C1, C2, p, q, loss_fun, epsilon, numItermax, stopThr, verbose, log)
+            C1, C2, p, q, loss_fun, epsilon, max_iter, stopThr, verbose, log)
     else:
         gw = gromov_wasserstein(C1, C2, p, q, loss_fun,
-                                epsilon, numItermax, stopThr, verbose, log)
+                                epsilon, max_iter, stopThr, verbose, log)
 
     if loss_fun == 'square_loss':
         gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw))
@@ -379,7 +379,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh
         return gw_dist
 
 
-def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
+def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False):
     """
     Returns the gromov-wasserstein barycenters of S measured similarity matrices
 
@@ -442,12 +442,12 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000
 
     error = []
 
-    while(err > stopThr and cpt < numItermax):
+    while(err > stopThr and cpt < max_iter):
 
         Cprev = C
 
         T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon,
-                                numItermax, 1e-5, verbose, log) for s in range(S)]
+                                max_iter, 1e-5, verbose, log) for s in range(S)]
 
         if loss_fun == 'square_loss':
             C = update_square_loss(p, lambdas, T, Cs)

test/test_gromov.py

@@ -10,18 +10,18 @@
 
 
 def test_gromov():
-    n = 50  # nb samples
+    n_samples = 50  # nb samples
 
     mu_s = np.array([0, 0])
     cov_s = np.array([[1, 0], [0, 1]])
 
-    xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
+    xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
 
-    xt = [xs[n - (i + 1)] for i in range(n)]
+    xt = [xs[n_samples - (i + 1)] for i in range(n_samples)]
     xt = np.array(xt)
 
-    p = ot.unif(n)
-    q = ot.unif(n)
+    p = ot.unif(n_samples)
+    q = ot.unif(n_samples)
 
     C1 = ot.dist(xs, xs)
     C2 = ot.dist(xt, xt)