diff options
| -rw-r--r-- | ot/da.py | 56 | ||||
| -rw-r--r-- | test/test_da.py | 51 |
2 files changed, 72 insertions, 35 deletions
@@ -15,6 +15,7 @@ from .lp import emd from .utils import unif, dist, kernel from .optim import cg from .optim import gcg +import warnings def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -921,15 +922,8 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): # proposal ############################################################################## -# from sklearn.base import BaseEstimator -# from sklearn.metrics import pairwise_distances - -############################################################################## -# adapted from scikit-learn - -import warnings -# from .externals.six import string_types, iteritems +# adapted from sklearn class BaseEstimator(object): """Base class for all estimators in scikit-learn @@ -1067,7 +1061,7 @@ def distribution_estimation_uniform(X): The uniform distribution estimated from X """ - return np.ones(X.shape[0]) / float(X.shape[0]) + return unif(X.shape[0]) class BaseTransport(BaseEstimator): @@ -1092,29 +1086,20 @@ class BaseTransport(BaseEstimator): """ # pairwise distance - Cost = dist(Xs, Xt, metric=self.metric) + self.Cost = dist(Xs, Xt, metric=self.metric) if self.mode == "semisupervised": print("TODO: modify cost matrix accordingly") pass # distribution estimation - mu_s = self.distribution_estimation(Xs) - mu_t = self.distribution_estimation(Xt) + self.mu_s = self.distribution_estimation(Xs) + self.mu_t = self.distribution_estimation(Xt) # store arrays of samples self.Xs = Xs self.Xt = Xt - # coupling estimation - if self.method == "sinkhorn": - self.gamma_ = sinkhorn( - a=mu_s, b=mu_t, M=Cost, reg=self.reg_e, - numItermax=self.max_iter, stopThr=self.tol, - verbose=self.verbose, log=self.log) - else: - print("TODO: implement the other methods") - return self def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): @@ -1157,8 +1142,7 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - # TODO: check whether Xs is new or not - if self.Xs == Xs: + if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] @@ -1169,7 +1153,9 @@ class BaseTransport(BaseEstimator): transp_Xs = np.dot(transp, self.Xt) else: # perform out of sample mapping - print("out of sample mapping not yet implemented") + print("Warning: out of sample mapping not yet implemented") + print("input data will be returned") + transp_Xs = Xs return transp_Xs @@ -1191,8 +1177,7 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - # TODO: check whether Xt is new or not - if self.Xt == Xt: + if np.array_equal(self.Xt, Xt): # perform standard barycentric mapping transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] @@ -1203,7 +1188,9 @@ class BaseTransport(BaseEstimator): transp_Xt = np.dot(transp_, self.Xs) else: # perform out of sample mapping - print("out of sample mapping not yet implemented") + print("Warning: out of sample mapping not yet implemented") + print("input data will be returned") + transp_Xt = Xt return transp_Xt @@ -1254,7 +1241,7 @@ class SinkhornTransport(BaseTransport): """ def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, - tol=10e-9, verbose=False, log=False, mapping="barycentric", + tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans'): @@ -1265,7 +1252,6 @@ class SinkhornTransport(BaseTransport): self.tol = tol self.verbose = verbose self.log = log - self.mapping = mapping self.metric = metric self.distribution_estimation = distribution_estimation self.method = "sinkhorn" @@ -1290,10 +1276,10 @@ class SinkhornTransport(BaseTransport): Returns self. """ - return super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) - + self = super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) -if __name__ == "__main__": - print("Small test") - - st = SinkhornTransport() + # coupling estimation + self.gamma_ = sinkhorn( + a=self.mu_s, b=self.mu_t, M=self.Cost, reg=self.reg_e, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) diff --git a/test/test_da.py b/test/test_da.py index dfba83f..e7b4ed1 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -6,6 +6,57 @@ import numpy as np import ot +from numpy.testing.utils import assert_allclose, assert_equal +from ot.datasets import get_data_classif +from ot.utils import unif + +np.random.seed(42) + + +def test_sinkhorn_transport(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.SinkhornTransport() + + # test its computed + clf.fit(Xs=Xs, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.gamma_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.gamma_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.gamma_, 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) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) def test_otda(): |