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"""Tests for module dr on Dimensionality Reduction """
# Author: Remi Flamary <remi.flamary@unice.fr>
# Minhui Huang <mhhuang@ucdavis.edu>
#
# License: MIT License
import numpy as np
import ot
import pytest
try: # test if autograd and pymanopt are installed
import ot.dr
nogo = False
except ImportError:
nogo = True
@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
def test_fda():
n_samples = 90 # nb samples in source and target datasets
np.random.seed(0)
# generate gaussian dataset
xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples)
n_features_noise = 8
xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise)))
p = 1
Pfda, projfda = ot.dr.fda(xs, ys, p)
projfda(xs)
np.testing.assert_allclose(np.sum(Pfda**2, 0), np.ones(p))
@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
def test_wda():
n_samples = 100 # nb samples in source and target datasets
np.random.seed(0)
# generate gaussian dataset
xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples)
n_features_noise = 8
xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise)))
p = 2
Pwda, projwda = ot.dr.wda(xs, ys, p, maxiter=10)
projwda(xs)
np.testing.assert_allclose(np.sum(Pwda**2, 0), np.ones(p))
@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
def test_wda_low_reg():
n_samples = 100 # nb samples in source and target datasets
np.random.seed(0)
# generate gaussian dataset
xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples)
n_features_noise = 8
xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise)))
p = 2
Pwda, projwda = ot.dr.wda(xs, ys, p, reg=0.01, maxiter=10, sinkhorn_method='sinkhorn_log')
projwda(xs)
np.testing.assert_allclose(np.sum(Pwda**2, 0), np.ones(p))
@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
def test_wda_normalized():
n_samples = 100 # nb samples in source and target datasets
np.random.seed(0)
# generate gaussian dataset
xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples)
n_features_noise = 8
xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise)))
p = 2
P0 = np.random.randn(10, p)
P0 /= P0.sum(0, keepdims=True)
Pwda, projwda = ot.dr.wda(xs, ys, p, maxiter=10, P0=P0, normalize=True)
projwda(xs)
np.testing.assert_allclose(np.sum(Pwda**2, 0), np.ones(p))
@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
def test_prw():
d = 100 # Dimension
n = 100 # Number samples
k = 3 # Subspace dimension
dim = 3
def fragmented_hypercube(n, d, dim):
assert dim <= d
assert dim >= 1
assert dim == int(dim)
a = (1. / n) * np.ones(n)
b = (1. / n) * np.ones(n)
# First measure : uniform on the hypercube
X = np.random.uniform(-1, 1, size=(n, d))
# Second measure : fragmentation
tmp_y = np.random.uniform(-1, 1, size=(n, d))
Y = tmp_y + 2 * np.sign(tmp_y) * np.array(dim * [1] + (d - dim) * [0])
return a, b, X, Y
a, b, X, Y = fragmented_hypercube(n, d, dim)
tau = 0.002
reg = 0.2
pi, U = ot.dr.projection_robust_wasserstein(X, Y, a, b, tau, reg=reg, k=k, maxiter=1000, verbose=1)
U0 = np.random.randn(d, k)
U0, _ = np.linalg.qr(U0)
pi, U = ot.dr.projection_robust_wasserstein(X, Y, a, b, tau, U0=U0, reg=reg, k=k, maxiter=1000, verbose=1)
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