laplace v1

ievred · ievred · commit 9200af5d7955 · 2020-04-02T15:29:12.000+02:00
diff --git a/ot/bregman.py b/ot/bregman.py
@@ -19,6 +19,7 @@
 from .utils import unif, dist
 from scipy.optimize import fmin_l_bfgs_b
 
+
 def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000,
              stopThr=1e-9, verbose=False, log=False, **kwargs):
     r"""
@@ -539,12 +540,12 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False,
             old_v = v[i_2]
             v[i_2] = b[i_2] / (K[:, i_2].T.dot(u))
             G[:, i_2] = u * K[:, i_2] * v[i_2]
-            #aviol = (G@one_m - a)
-            #aviol_2 = (G.T@one_n - b)
+            # aviol = (G@one_m - a)
+            # aviol_2 = (G.T@one_n - b)
             viol += (-old_v + v[i_2]) * K[:, i_2] * u
             viol_2[i_2] = v[i_2] * K[:, i_2].dot(u) - b[i_2]
 
-            #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2)))
+            # print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2)))
 
         if stopThr_val <= stopThr:
             break
@@ -715,7 +716,7 @@ def get_Gamma(alpha, beta, u, v):
         if np.abs(u).max() > tau or np.abs(v).max() > tau:
             if n_hists:
                 alpha, beta = alpha + reg * \
-                    np.max(np.log(u), 1), beta + reg * np.max(np.log(v))
+                              np.max(np.log(u), 1), beta + reg * np.max(np.log(v))
             else:
                 alpha, beta = alpha + reg * np.log(u), beta + reg * np.log(v)
                 if n_hists:
@@ -940,7 +941,7 @@ def get_reg(n):  # exponential decreasing
             # the 10th iterations
             transp = G
             err = np.linalg.norm(
-                (np.sum(transp, axis=0) - b))**2 + np.linalg.norm((np.sum(transp, axis=1) - a))**2
+                (np.sum(transp, axis=0) - b)) ** 2 + np.linalg.norm((np.sum(transp, axis=1) - a)) ** 2
             if log:
                 log['err'].append(err)
 
@@ -966,7 +967,7 @@ def get_reg(n):  # exponential decreasing
 
 def geometricBar(weights, alldistribT):
     """return the weighted geometric mean of distributions"""
-    assert(len(weights) == alldistribT.shape[1])
+    assert (len(weights) == alldistribT.shape[1])
     return np.exp(np.dot(np.log(alldistribT), weights.T))
 
 
@@ -1108,7 +1109,7 @@ def barycenter_sinkhorn(A, M, reg, weights=None, numItermax=1000,
     if weights is None:
         weights = np.ones(A.shape[1]) / A.shape[1]
     else:
-        assert(len(weights) == A.shape[1])
+        assert (len(weights) == A.shape[1])
 
     if log:
         log = {'err': []}
@@ -1206,7 +1207,7 @@ def barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000,
     if weights is None:
         weights = np.ones(n_hists) / n_hists
     else:
-        assert(len(weights) == A.shape[1])
+        assert (len(weights) == A.shape[1])
 
     if log:
         log = {'err': []}
@@ -1334,7 +1335,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000,
     if weights is None:
         weights = np.ones(A.shape[0]) / A.shape[0]
     else:
-        assert(len(weights) == A.shape[0])
+        assert (len(weights) == A.shape[0])
 
     if log:
         log = {'err': []}
@@ -1350,11 +1351,11 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000,
     # this is equivalent to blurring on horizontal then vertical directions
     t = np.linspace(0, 1, A.shape[1])
     [Y, X] = np.meshgrid(t, t)
-    xi1 = np.exp(-(X - Y)**2 / reg)
+    xi1 = np.exp(-(X - Y) ** 2 / reg)
 
     t = np.linspace(0, 1, A.shape[2])
     [Y, X] = np.meshgrid(t, t)
-    xi2 = np.exp(-(X - Y)**2 / reg)
+    xi2 = np.exp(-(X - Y) ** 2 / reg)
 
     def K(x):
         return np.dot(np.dot(xi1, x), xi2)
@@ -1501,6 +1502,7 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000,
     else:
         return np.sum(K0, axis=1)
 
+
 def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100,
                      stopThr=1e-6, verbose=False, log=False, **kwargs):
     r'''Joint OT and proportion estimation for multi-source target shift as proposed in [27]
@@ -1658,6 +1660,7 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100,
     else:
         return couplings, bary
 
+
 def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean',
                        numIterMax=10000, stopThr=1e-9, verbose=False,
                        log=False, **kwargs):
@@ -1749,7 +1752,8 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean',
         return pi
 
 
-def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
+def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9,
+                        verbose=False, log=False, **kwargs):
     r'''
     Solve the entropic regularization optimal transport problem from empirical
     data and return the OT loss
@@ -1831,14 +1835,17 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num
     M = dist(X_s, X_t, metric=metric)
 
     if log:
-        sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs)
+        sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log,
+                                       **kwargs)
         return sinkhorn_loss, log
     else:
-        sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs)
+        sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log,
+                                  **kwargs)
         return sinkhorn_loss
 
 
-def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
+def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9,
+                                  verbose=False, log=False, **kwargs):
     r'''
     Compute the sinkhorn divergence loss from empirical data
 
@@ -1924,11 +1931,14 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli
     .. [23] Aude Genevay, Gabriel Peyré, Marco Cuturi, Learning Generative Models with Sinkhorn Divergences,  Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018
     '''
     if log:
-        sinkhorn_loss_ab, log_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+        sinkhorn_loss_ab, log_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax,
+                                                       stopThr=1e-9, verbose=verbose, log=log, **kwargs)
 
-        sinkhorn_loss_a, log_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+        sinkhorn_loss_a, log_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax,
+                                                     stopThr=1e-9, verbose=verbose, log=log, **kwargs)
 
-        sinkhorn_loss_b, log_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+        sinkhorn_loss_b, log_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax,
+                                                     stopThr=1e-9, verbose=verbose, log=log, **kwargs)
 
         sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b)
 
@@ -1943,11 +1953,14 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli
         return max(0, sinkhorn_div), log
 
     else:
-        sinkhorn_loss_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+        sinkhorn_loss_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9,
+                                               verbose=verbose, log=log, **kwargs)
 
-        sinkhorn_loss_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+        sinkhorn_loss_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9,
+                                              verbose=verbose, log=log, **kwargs)
 
-        sinkhorn_loss_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)
+        sinkhorn_loss_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9,
+                                              verbose=verbose, log=log, **kwargs)
 
         sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b)
         return max(0, sinkhorn_div)
@@ -2039,7 +2052,8 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res
     try:
         import bottleneck
     except ImportError:
-        warnings.warn("Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.")
+        warnings.warn(
+            "Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.")
         bottleneck = np
 
     a = np.asarray(a, dtype=np.float64)
@@ -2173,10 +2187,11 @@ def projection(u, epsilon):
 
         # box constraints in L-BFGS-B (see Proposition 1 in [26])
         bounds_u = [(max(a_I_min / ((nt - nt_budget) * epsilon + nt_budget * (b_J_max / (
-            ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget
+                ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget
 
-        bounds_v = [(max(b_J_min / ((ns - ns_budget) * epsilon + ns_budget * (kappa * a_I_max / (nt * epsilon * K_min))),
-                         epsilon * kappa), b_J_max / (ns * epsilon * K_min))] * nt_budget
+        bounds_v = [(
+                    max(b_J_min / ((ns - ns_budget) * epsilon + ns_budget * (kappa * a_I_max / (nt * epsilon * K_min))),
+                        epsilon * kappa), b_J_max / (ns * epsilon * K_min))] * nt_budget
 
         # pre-calculated constants for the objective
         vec_eps_IJc = epsilon * kappa * (K_IJc * np.ones(nt - nt_budget).reshape((1, -1))).sum(axis=1)
@@ -2225,7 +2240,8 @@ def restricted_sinkhorn(usc, vsc, max_iter=5):
         return usc, vsc
 
     def screened_obj(usc, vsc):
-        part_IJ = np.dot(np.dot(usc, K_IJ), vsc) - kappa * np.dot(a_I, np.log(usc)) - (1. / kappa) * np.dot(b_J, np.log(vsc))
+        part_IJ = np.dot(np.dot(usc, K_IJ), vsc) - kappa * np.dot(a_I, np.log(usc)) - (1. / kappa) * np.dot(b_J,
+                                                                                                            np.log(vsc))
         part_IJc = np.dot(usc, vec_eps_IJc)
         part_IcJ = np.dot(vec_eps_IcJ, vsc)
         psi_epsilon = part_IJ + part_IJc + part_IcJ
@@ -2247,9 +2263,9 @@ def bfgspost(theta):
         g = np.hstack([g_u, g_v])
         return f, g
 
-    #----------------------------------------------------------------------------------------------------------------#
+    # ----------------------------------------------------------------------------------------------------------------#
     #                                           Step 2: L-BFGS-B solver                                              #
-    #----------------------------------------------------------------------------------------------------------------#
+    # ----------------------------------------------------------------------------------------------------------------#
 
     u0, v0 = restricted_sinkhorn(u0, v0)
     theta0 = np.hstack([u0, v0])
diff --git a/ot/datasets.py b/ot/datasets.py
@@ -30,7 +30,7 @@ def make_1D_gauss(n, m, s):
         1D histogram for a gaussian distribution
     """
     x = np.arange(n, dtype=np.float64)
-    h = np.exp(-(x - m)**2 / (2 * s**2))
+    h = np.exp(-(x - m) ** 2 / (2 * s ** 2))
     return h / h.sum()
 
 
@@ -80,7 +80,7 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None):
     return make_2D_samples_gauss(n, m, sigma, random_state=None)
 
 
-def make_data_classif(dataset, n, nz=.5, theta=0, p = .5, random_state=None, **kwargs):
+def make_data_classif(dataset, n, nz=.5, theta=0, p=.5, random_state=None, **kwargs):
     """Dataset generation for classification problems
 
     Parameters
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
@@ -2,8 +2,6 @@
 """
 Solvers for the original linear program OT problem
 
-
-
 """
 
 # Author: Remi Flamary <remi.flamary@unice.fr>
@@ -18,7 +16,7 @@
 from .import cvx
 
 # import compiled emd
-from .emd_wrap import emd_c, check_result, emd_1d_sorted
+#from .emd_wrap import emd_c, check_result, emd_1d_sorted
 from ..utils import parmap
 from .cvx import barycenter
 from ..utils import dist
diff --git a/ot/plot.py b/ot/plot.py
@@ -78,9 +78,10 @@ def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs):
     thr : float, optional
         threshold above which the line is drawn
     **kwargs : dict
-        paameters given to the plot functions (default color is black if
+        parameters given to the plot functions (default color is black if
         nothing given)
     """
+
     if ('color' not in kwargs) and ('c' not in kwargs):
         kwargs['color'] = 'k'
     mx = G.max()
