Merge pull request #13 from agramfort/pimp_it_up

[MRG] flake8 + pimp example figures
Fixes #2 
Big thanks to @agramfort  for the counselling and PEP8 cleanup

Author: rflamary

.gitignore

@@ -97,3 +97,6 @@ ENV/
 
 # Rope project settings
 .ropeproject
+
+# Mac stuff
+.DS_Store

.travis.yml

@@ -16,6 +16,10 @@ before_install:
 # command to install dependencies
 install:
   - pip install -r requirements.txt
+  - pip install flake8 pytest
   - python setup.py install
-# command to run tests
-script: python test/test_load_module.py -v
+# command to run tests + check syntax style
+script:
+  - python test/test_load_module.py -v
+  - flake8 examples/ ot/ test/
+  # - py.test ot test

examples/plot_OTDA_2D.py

@@ -11,110 +11,112 @@
 import ot
 
 
-
 #%% parameters
 
-n=150 # nb bins
+n = 150  # nb bins
 
-xs,ys=ot.datasets.get_data_classif('3gauss',n)
-xt,yt=ot.datasets.get_data_classif('3gauss2',n)
+xs, ys = ot.datasets.get_data_classif('3gauss', n)
+xt, yt = ot.datasets.get_data_classif('3gauss2', n)
 
-a,b = ot.unif(n),ot.unif(n)
+a, b = ot.unif(n), ot.unif(n)
 # loss matrix
-M=ot.dist(xs,xt)
-#M/=M.max()
+M = ot.dist(xs, xt)
+# M/=M.max()
 
 #%% plot samples
 
 pl.figure(1)
-
-pl.subplot(2,2,1)
-pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')
+pl.subplot(2, 2, 1)
+pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
 pl.legend(loc=0)
 pl.title('Source  distributions')
 
-pl.subplot(2,2,2)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')
+pl.subplot(2, 2, 2)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
 pl.legend(loc=0)
 pl.title('target  distributions')
 
 pl.figure(2)
-pl.imshow(M,interpolation='nearest')
+pl.imshow(M, interpolation='nearest')
 pl.title('Cost matrix M')
 
 
 #%% OT estimation
 
 # EMD
-G0=ot.emd(a,b,M)
+G0 = ot.emd(a, b, M)
 
 # sinkhorn
-lambd=1e-1
-Gs=ot.sinkhorn(a,b,M,lambd)
+lambd = 1e-1
+Gs = ot.sinkhorn(a, b, M, lambd)
 
 
 # Group lasso regularization
-reg=1e-1
-eta=1e0
-Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta)
+reg = 1e-1
+eta = 1e0
+Gg = ot.da.sinkhorn_lpl1_mm(a, ys.astype(np.int), b, M, reg, eta)
 
 
 #%% visu matrices
 
 pl.figure(3)
 
-pl.subplot(2,3,1)
-pl.imshow(G0,interpolation='nearest')
+pl.subplot(2, 3, 1)
+pl.imshow(G0, interpolation='nearest')
 pl.title('OT matrix ')
 
-pl.subplot(2,3,2)
-pl.imshow(Gs,interpolation='nearest')
+pl.subplot(2, 3, 2)
+pl.imshow(Gs, interpolation='nearest')
 pl.title('OT matrix Sinkhorn')
 
-pl.subplot(2,3,3)
-pl.imshow(Gg,interpolation='nearest')
+pl.subplot(2, 3, 3)
+pl.imshow(Gg, interpolation='nearest')
 pl.title('OT matrix Group lasso')
 
-pl.subplot(2,3,4)
-ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])
-pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')
+pl.subplot(2, 3, 4)
+ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])
+pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
 
 
-pl.subplot(2,3,5)
-ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1])
-pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')
+pl.subplot(2, 3, 5)
+ot.plot.plot2D_samples_mat(xs, xt, Gs, c=[.5, .5, 1])
+pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
 
-pl.subplot(2,3,6)
-ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1])
-pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')
+pl.subplot(2, 3, 6)
+ot.plot.plot2D_samples_mat(xs, xt, Gg, c=[.5, .5, 1])
+pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
+pl.tight_layout()
 
 #%% sample interpolation
 
-xst0=n*G0.dot(xt)
-xsts=n*Gs.dot(xt)
-xstg=n*Gg.dot(xt)
-
-pl.figure(4)
-pl.subplot(2,3,1)
-
+xst0 = n * G0.dot(xt)
+xsts = n * Gs.dot(xt)
+xstg = n * Gg.dot(xt)
 
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)
-pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)
+pl.figure(4, figsize=(8, 3))
+pl.subplot(1, 3, 1)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.5)
+pl.scatter(xst0[:, 0], xst0[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
 pl.title('Interp samples')
 pl.legend(loc=0)
 
-pl.subplot(2,3,2)
-
-
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)
-pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)
+pl.subplot(1, 3, 2)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.5)
+pl.scatter(xsts[:, 0], xsts[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
 pl.title('Interp samples Sinkhorn')
 
-pl.subplot(2,3,3)
-
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)
-pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)
-pl.title('Interp samples Grouplasso')
+pl.subplot(1, 3, 3)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.5)
+pl.scatter(xstg[:, 0], xstg[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.title('Interp samples Grouplasso')
+pl.tight_layout()
+pl.show()

examples/plot_OTDA_classes.py

@@ -10,103 +10,104 @@
 import ot
 
 
-
-
 #%% parameters
 
-n=150 # nb samples in source and target datasets
-
-xs,ys=ot.datasets.get_data_classif('3gauss',n)
-xt,yt=ot.datasets.get_data_classif('3gauss2',n)
-
+n = 150  # nb samples in source and target datasets
 
+xs, ys = ot.datasets.get_data_classif('3gauss', n)
+xt, yt = ot.datasets.get_data_classif('3gauss2', n)
 
 
 #%% plot samples
 
-pl.figure(1)
+pl.figure(1, figsize=(6.4, 3))
 
-pl.subplot(2,2,1)
-pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')
+pl.subplot(1, 2, 1)
+pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples')
 pl.legend(loc=0)
 pl.title('Source  distributions')
 
-pl.subplot(2,2,2)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')
+pl.subplot(1, 2, 2)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples')
 pl.legend(loc=0)
 pl.title('target  distributions')
 
 
 #%% OT estimation
 
 # LP problem
-da_emd=ot.da.OTDA()     # init class
-da_emd.fit(xs,xt)       # fit distributions
-xst0=da_emd.interp()    # interpolation of source samples
-
+da_emd = ot.da.OTDA()     # init class
+da_emd.fit(xs, xt)       # fit distributions
+xst0 = da_emd.interp()    # interpolation of source samples
 
 # sinkhorn regularization
-lambd=1e-1
-da_entrop=ot.da.OTDA_sinkhorn()
-da_entrop.fit(xs,xt,reg=lambd)
-xsts=da_entrop.interp()
+lambd = 1e-1
+da_entrop = ot.da.OTDA_sinkhorn()
+da_entrop.fit(xs, xt, reg=lambd)
+xsts = da_entrop.interp()
 
 # non-convex Group lasso regularization
-reg=1e-1
-eta=1e0
-da_lpl1=ot.da.OTDA_lpl1()
-da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta)
-xstg=da_lpl1.interp()
-
+reg = 1e-1
+eta = 1e0
+da_lpl1 = ot.da.OTDA_lpl1()
+da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta)
+xstg = da_lpl1.interp()
 
 # True Group lasso regularization
-reg=1e-1
-eta=2e0
-da_l1l2=ot.da.OTDA_l1l2()
-da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True)
-xstgl=da_l1l2.interp()
-
+reg = 1e-1
+eta = 2e0
+da_l1l2 = ot.da.OTDA_l1l2()
+da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True)
+xstgl = da_l1l2.interp()
 
 #%% plot interpolated source samples
-pl.figure(4,(15,8))
 
-param_img={'interpolation':'nearest','cmap':'jet'}
+param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}
 
-pl.subplot(2,4,1)
-pl.imshow(da_emd.G,**param_img)
+pl.figure(2, figsize=(8, 4.5))
+pl.subplot(2, 4, 1)
+pl.imshow(da_emd.G, **param_img)
 pl.title('OT matrix')
 
+pl.subplot(2, 4, 2)
+pl.imshow(da_entrop.G, **param_img)
+pl.title('OT matrix\nsinkhorn')
 
-pl.subplot(2,4,2)
-pl.imshow(da_entrop.G,**param_img)
-pl.title('OT matrix sinkhorn')
-
-pl.subplot(2,4,3)
-pl.imshow(da_lpl1.G,**param_img)
-pl.title('OT matrix non-convex Group Lasso')
-
-pl.subplot(2,4,4)
-pl.imshow(da_l1l2.G,**param_img)
-pl.title('OT matrix Group Lasso')
+pl.subplot(2, 4, 3)
+pl.imshow(da_lpl1.G, **param_img)
+pl.title('OT matrix\nnon-convex Group Lasso')
 
+pl.subplot(2, 4, 4)
+pl.imshow(da_l1l2.G, **param_img)
+pl.title('OT matrix\nGroup Lasso')
 
-pl.subplot(2,4,5)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
-pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)
+pl.subplot(2, 4, 5)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(xst0[:, 0], xst0[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
 pl.title('Interp samples')
 pl.legend(loc=0)
 
-pl.subplot(2,4,6)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
-pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)
-pl.title('Interp samples Sinkhorn')
-
-pl.subplot(2,4,7)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
-pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)
-pl.title('Interp samples non-convex Group Lasso')
-
-pl.subplot(2,4,8)
-pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
-pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30)
-pl.title('Interp samples Group Lasso')
+pl.subplot(2, 4, 6)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(xsts[:, 0], xsts[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.title('Interp samples\nSinkhorn')
+
+pl.subplot(2, 4, 7)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(xstg[:, 0], xstg[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.title('Interp samples\nnon-convex Group Lasso')
+
+pl.subplot(2, 4, 8)
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o',
+           label='Target samples', alpha=0.3)
+pl.scatter(xstgl[:, 0], xstgl[:, 1], c=ys,
+           marker='+', label='Transp samples', s=30)
+pl.title('Interp samples\nGroup Lasso')
+pl.tight_layout()
+pl.show()