add test da 58% coverage

Author: rflamary

test/test_da.py

@@ -0,0 +1,67 @@
+
+
+import ot
+import numpy as np
+
+# import pytest
+
+
+def test_OTDA():
+
+    n = 150  # nb bins
+
+    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)
+
+    # LP problem
+    da_emd = ot.da.OTDA()     # init class
+    da_emd.fit(xs, xt)       # fit distributions
+    da_emd.interp()    # interpolation of source samples
+    da_emd.predict(xs)    # interpolation of source samples
+
+    assert np.allclose(a, np.sum(da_emd.G, 1))
+    assert np.allclose(b, np.sum(da_emd.G, 0))
+
+    # sinkhorn regularization
+    lambd = 1e-1
+    da_entrop = ot.da.OTDA_sinkhorn()
+    da_entrop.fit(xs, xt, reg=lambd)
+    da_entrop.interp()
+    da_entrop.predict(xs)
+
+    assert np.allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3)
+    assert np.allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3)
+
+    # 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)
+    da_lpl1.interp()
+    da_lpl1.predict(xs)
+
+    assert np.allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3)
+    assert np.allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3)
+
+    # 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)
+    da_l1l2.interp()
+    da_l1l2.predict(xs)
+
+    assert np.allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3)
+    assert np.allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3)
+
+    # linear mapping
+    da_emd = ot.da.OTDA_mapping_linear()     # init class
+    da_emd.fit(xs, xt, numItermax=10)       # fit distributions
+    da_emd.predict(xs)    # interpolation of source samples
+
+    # nonlinear mapping
+    da_emd = ot.da.OTDA_mapping_kernel()     # init class
+    da_emd.fit(xs, xt, numItermax=10)       # fit distributions
+    da_emd.predict(xs)    # interpolation of source samples