add linear mapping test + autopep8

Author: rflamary

examples/plot_otda_linear_mapping.py

@@ -9,7 +9,6 @@
 import numpy as np
 import pylab as pl
 import ot
-from scipy import ndimage
 
 ##############################################################################
 # Generate data
@@ -87,8 +86,8 @@ def minmax(I):
 
 
 # Loading images
-I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
-I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
+I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256
+I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
 
 
 X1 = im2mat(I1)

ot/bregman.py

@@ -11,7 +11,8 @@
 import numpy as np
 
 
-def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs):
+def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000,
+             stopThr=1e-9, verbose=False, log=False, **kwargs):
     u"""
     Solve the entropic regularization optimal transport problem and return the OT matrix
 
@@ -120,7 +121,8 @@ def sink():
     return sink()
 
 
-def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs):
+def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000,
+              stopThr=1e-9, verbose=False, log=False, **kwargs):
     u"""
     Solve the entropic regularization optimal transport problem and return the loss
 
@@ -233,7 +235,8 @@ def sink():
     return sink()
 
 
-def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs):
+def sinkhorn_knopp(a, b, M, reg, numItermax=1000,
+                   stopThr=1e-9, verbose=False, log=False, **kwargs):
     """
     Solve the entropic regularization optimal transport problem and return the OT matrix
 
@@ -403,7 +406,8 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, l
             return u.reshape((-1, 1)) * K * v.reshape((1, -1))
 
 
-def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=20, log=False, **kwargs):
+def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9,
+                        warmstart=None, verbose=False, print_period=20, log=False, **kwargs):
     """
     Solve the entropic regularization OT problem with log stabilization
 
@@ -526,11 +530,13 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, wa
 
     def get_K(alpha, beta):
         """log space computation"""
-        return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg)
+        return np.exp(-(M - alpha.reshape((na, 1)) -
+                        beta.reshape((1, nb))) / reg)
 
     def get_Gamma(alpha, beta, u, v):
         """log space gamma computation"""
-        return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb))))
+        return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) /
+                      reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb))))
 
     # print(np.min(K))
 
@@ -620,7 +626,8 @@ def get_Gamma(alpha, beta, u, v):
             return get_Gamma(alpha, beta, u, v)
 
 
-def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInnerItermax=100, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=10, log=False, **kwargs):
+def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInnerItermax=100,
+                             tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=10, log=False, **kwargs):
     """
     Solve the entropic regularization optimal transport problem with log
     stabilization and epsilon scaling.
@@ -739,7 +746,8 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne
 
     def get_K(alpha, beta):
         """log space computation"""
-        return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg)
+        return np.exp(-(M - alpha.reshape((na, 1)) -
+                        beta.reshape((1, nb))) / reg)
 
     # print(np.min(K))
     def get_reg(n):  # exponential decreasing
@@ -811,7 +819,8 @@ def projC(gamma, q):
     return np.multiply(gamma, q / np.maximum(np.sum(gamma, axis=0), 1e-10))
 
 
-def barycenter(A, M, reg, weights=None, numItermax=1000, stopThr=1e-4, verbose=False, log=False):
+def barycenter(A, M, reg, weights=None, numItermax=1000,
+               stopThr=1e-4, verbose=False, log=False):
     """Compute the entropic regularized wasserstein barycenter of distributions A
 
      The function solves the following optimization problem:
@@ -904,7 +913,8 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, stopThr=1e-4, verbose=F
         return geometricBar(weights, UKv)
 
 
-def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verbose=False, log=False):
+def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000,
+          stopThr=1e-3, verbose=False, log=False):
     """
     Compute the unmixing of an observation with a given dictionary using Wasserstein distance
 

ot/lp/__init__.py

@@ -107,7 +107,8 @@ def emd(a, b, M, numItermax=100000, log=False):
     return G
 
 
-def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=False, return_matrix=False):
+def emd2(a, b, M, processes=multiprocessing.cpu_count(),
+         numItermax=100000, log=False, return_matrix=False):
     """Solves the Earth Movers distance problem and returns the loss
 
     .. math::

ot/optim.py

@@ -15,7 +15,8 @@
 # The corresponding scipy function does not work for matrices
 
 
-def line_search_armijo(f, xk, pk, gfk, old_fval, args=(), c1=1e-4, alpha0=0.99):
+def line_search_armijo(f, xk, pk, gfk, old_fval,
+                       args=(), c1=1e-4, alpha0=0.99):
     """
     Armijo linesearch function that works with matrices
 
@@ -71,7 +72,8 @@ def phi(alpha1):
     return alpha, fc[0], phi1
 
 
-def cg(a, b, M, reg, f, df, G0=None, numItermax=200, stopThr=1e-9, verbose=False, log=False):
+def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
+       stopThr=1e-9, verbose=False, log=False):
     """
     Solve the general regularized OT problem with conditional gradient
 
@@ -202,7 +204,8 @@ def cost(G):
         return G
 
 
-def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, numInnerItermax=200, stopThr=1e-9, verbose=False, log=False):
+def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10,
+        numInnerItermax=200, stopThr=1e-9, verbose=False, log=False):
     """
     Solve the general regularized OT problem with the generalized conditional gradient
 

ot/utils.py

@@ -316,7 +316,7 @@ def _is_deprecated(func):
         closures = []
     is_deprecated = ('deprecated' in ''.join([c.cell_contents
                                               for c in closures
-                     if isinstance(c.cell_contents, str)]))
+                                              if isinstance(c.cell_contents, str)]))
     return is_deprecated
 
 

test/test_da.py

@@ -444,6 +444,24 @@ def test_mapping_transport_class():
     assert len(otda.log_.keys()) != 0
 
 
+def test_linear_mapping():
+
+    ns = 150
+    nt = 200
+
+    Xs, ys = get_data_classif('3gauss', ns)
+    Xt, yt = get_data_classif('3gauss2', nt)
+
+    A, b = ot.da.OT_mapping_linear(Xs, Xt)
+
+    Xst = Xs.dot(A) + b
+
+    Ct = np.cov(Xt.T)
+    Cst = np.cov(Xst.T)
+
+    np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)
+
+
 def test_otda():
 
     n_samples = 150  # nb samples