def test_mp_empiric_dist_equal_sim(self):
self.setUpMod('rnd')
sim = 1. - self.dist
mp_dist = mutual_proximity_empiric(self.dist, 'distance')
mp_sim = mutual_proximity_empiric(sim, 'similarity')
return np.testing.assert_array_almost_equal(mp_dist,
1. - mp_sim,
decimal=7)
def test_mp_empiric_sample(self):
"""Test MP Emp Sample equals MP Emp when sample == population"""
self.setUpMod('toy')
mp_dist = mutual_proximity_empiric(self.dist, 'distance')
y = np.array([0, 1, 2, 3, 4])
mp_sample_dist = mutual_proximity_empiric(D=self.dist,
sample_ind=y,
metric='distance')
return np.testing.assert_array_almost_equal(mp_dist,
mp_sample_dist,
decimal=7)
def test_mp_empiric_sparse_equal_dense(self):
self.setUpMod('rnd')
sim_dense = 1. - self.dist
sim_sparse = csr_matrix(sim_dense)
mp_dense = mutual_proximity_empiric(sim_dense, 'similarity')
mp_sparse = mutual_proximity_empiric(sim_sparse,
'similarity',
verbose=1,
n_jobs=4)
return np.testing.assert_array_almost_equal(mp_dense,
mp_sparse.toarray(),
decimal=7)
def test_mp_empiric(self):
"""Test MP Empiric for toy example (ground truth calc by hand)"""
self.setUpMod('toy')
mp_dist_calc = mutual_proximity_empiric(self.dist,
'distance',
verbose=1)
return np.testing.assert_array_almost_equal(mp_dist_calc,
self.mp_dist_truth,
decimal=7)
def test_mp_empiric_symmetric(self):
self.setUpMod('rnd')
mp_dist = mutual_proximity_empiric(self.dist)
return np.testing.assert_array_almost_equal(mp_dist,
mp_dist.T,
decimal=14)
def test_mp_empiric_all_zero_self_distances(self):
self.setUpMod('rnd')
mp_dist_calc = mutual_proximity_empiric(self.dist)
mp_self_distances_all_zero = np.all(mp_dist_calc.diagonal() == 0.)
return self.assertTrue(mp_self_distances_all_zero)