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authorSlasnista <stan.chambon@gmail.com>2017-08-23 14:11:13 +0200
committerSlasnista <stan.chambon@gmail.com>2017-08-23 14:11:13 +0200
commit326d163db029515c338a963978a5d95948f78c29 (patch)
tree0a40e7d918e429f6d17fdb1ab4656f2a2c31f07c
parent791a4a6f215033a75d5f56cd16fe2412301bec14 (diff)
test functions for MappingTransport Class
-rw-r--r--ot/da.py18
-rw-r--r--test/test_da.py117
2 files changed, 125 insertions, 10 deletions
diff --git a/ot/da.py b/ot/da.py
index 0c83ae6..3ccb1b3 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -1665,8 +1665,14 @@ class MappingTransport(BaseEstimator):
Attributes
----------
- coupling_ : the optimal coupling
- mapping_ : the mapping associated
+ coupling_ : array-like, shape (n_source_samples, n_features)
+ The optimal coupling
+ mapping_ : array-like, shape (n_features (+ 1), n_features)
+ (if bias) for kernel == linear
+ The associated mapping
+
+ array-like, shape (n_source_samples (+ 1), n_features)
+ (if bias) for kernel == gaussian
References
----------
@@ -1679,20 +1685,22 @@ class MappingTransport(BaseEstimator):
def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean",
kernel="linear", sigma=1, max_iter=100, tol=1e-5,
- max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False):
+ max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False,
+ verbose2=False):
self.metric = metric
self.mu = mu
self.eta = eta
self.bias = bias
self.kernel = kernel
- self.sigma
+ self.sigma = sigma
self.max_iter = max_iter
self.tol = tol
self.max_inner_iter = max_inner_iter
self.inner_tol = inner_tol
self.log = log
self.verbose = verbose
+ self.verbose2 = verbose2
def fit(self, Xs=None, ys=None, Xt=None, yt=None):
"""Builds an optimal coupling and estimates the associated mapping
@@ -1712,7 +1720,7 @@ class MappingTransport(BaseEstimator):
Returns
-------
self : object
- Returns self.
+ Returns self
"""
self.Xs = Xs
diff --git a/test/test_da.py b/test/test_da.py
index 196f4c4..162f681 100644
--- a/test/test_da.py
+++ b/test/test_da.py
@@ -264,6 +264,112 @@ def test_emd_transport_class():
assert n_unsup != n_semisup, "semisupervised mode not working"
+def test_mapping_transport_class():
+ """test_mapping_transport
+ """
+
+ ns = 150
+ nt = 200
+
+ Xs, ys = get_data_classif('3gauss', ns)
+ Xt, yt = get_data_classif('3gauss2', nt)
+ Xs_new, _ = get_data_classif('3gauss', ns + 1)
+
+ ##########################################################################
+ # kernel == linear mapping tests
+ ##########################################################################
+
+ # check computation and dimensions if bias == False
+ clf = ot.da.MappingTransport(kernel="linear", bias=False)
+ clf.fit(Xs=Xs, Xt=Xt)
+
+ assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert_equal(clf.mapping_.shape, ((Xs.shape[1], Xt.shape[1])))
+
+ # test margin constraints
+ mu_s = unif(ns)
+ mu_t = unif(nt)
+ assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3)
+ assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3)
+
+ # test transform
+ transp_Xs = clf.transform(Xs=Xs)
+ assert_equal(transp_Xs.shape, Xs.shape)
+
+ transp_Xs_new = clf.transform(Xs_new)
+
+ # check that the oos method is working
+ assert_equal(transp_Xs_new.shape, Xs_new.shape)
+
+ # check computation and dimensions if bias == True
+ clf = ot.da.MappingTransport(kernel="linear", bias=True)
+ clf.fit(Xs=Xs, Xt=Xt)
+ assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert_equal(clf.mapping_.shape, ((Xs.shape[1] + 1, Xt.shape[1])))
+
+ # test margin constraints
+ mu_s = unif(ns)
+ mu_t = unif(nt)
+ assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3)
+ assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3)
+
+ # test transform
+ transp_Xs = clf.transform(Xs=Xs)
+ assert_equal(transp_Xs.shape, Xs.shape)
+
+ transp_Xs_new = clf.transform(Xs_new)
+
+ # check that the oos method is working
+ assert_equal(transp_Xs_new.shape, Xs_new.shape)
+
+ ##########################################################################
+ # kernel == gaussian mapping tests
+ ##########################################################################
+
+ # check computation and dimensions if bias == False
+ clf = ot.da.MappingTransport(kernel="gaussian", bias=False)
+ clf.fit(Xs=Xs, Xt=Xt)
+
+ assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert_equal(clf.mapping_.shape, ((Xs.shape[0], Xt.shape[1])))
+
+ # test margin constraints
+ mu_s = unif(ns)
+ mu_t = unif(nt)
+ assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3)
+ assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3)
+
+ # test transform
+ transp_Xs = clf.transform(Xs=Xs)
+ assert_equal(transp_Xs.shape, Xs.shape)
+
+ transp_Xs_new = clf.transform(Xs_new)
+
+ # check that the oos method is working
+ assert_equal(transp_Xs_new.shape, Xs_new.shape)
+
+ # check computation and dimensions if bias == True
+ clf = ot.da.MappingTransport(kernel="gaussian", bias=True)
+ clf.fit(Xs=Xs, Xt=Xt)
+ assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert_equal(clf.mapping_.shape, ((Xs.shape[0] + 1, Xt.shape[1])))
+
+ # test margin constraints
+ mu_s = unif(ns)
+ mu_t = unif(nt)
+ assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3)
+ assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3)
+
+ # test transform
+ transp_Xs = clf.transform(Xs=Xs)
+ assert_equal(transp_Xs.shape, Xs.shape)
+
+ transp_Xs_new = clf.transform(Xs_new)
+
+ # check that the oos method is working
+ assert_equal(transp_Xs_new.shape, Xs_new.shape)
+
+
def test_otda():
n_samples = 150 # nb samples
@@ -326,9 +432,10 @@ def test_otda():
da_emd.predict(xs) # interpolation of source samples
-# if __name__ == "__main__":
+if __name__ == "__main__":
-# test_sinkhorn_transport_class()
-# test_emd_transport_class()
-# test_sinkhorn_l1l2_transport_class()
-# test_sinkhorn_lpl1_transport_class()
+ # test_sinkhorn_transport_class()
+ # test_emd_transport_class()
+ # test_sinkhorn_l1l2_transport_class()
+ # test_sinkhorn_lpl1_transport_class()
+ test_mapping_transport_class()