def test_dis_sim_local(self):
"""Test whether hubness and k-NN accuracy improve for dexter"""
h_orig = hubness(self.distance)[0]
acc_orig = score(self.distance, self.target)[0][0, 0]
dist_dsl = dis_sim_local(self.vectors, k=50)
h_dsl = hubness(dist_dsl)[0]
acc_dsl = score(dist_dsl, self.target)[0][0, 0]
result = (h_orig / h_dsl > 10) & (acc_dsl - acc_orig > 0.03)
return self.assertTrue(result)
def test_dis_sim_global(self):
"""Test whether hubness and k-NN accuracy improve for dexter"""
h_orig = hubness(self.distance)[0]
acc_orig = score(self.distance, self.target)[0][0, 0]
dist_dsg = dis_sim_global(self.vectors)
h_dsg = hubness(dist_dsg)[0]
acc_dsg = score(dist_dsg, self.target)[0][0, 0]
result = (h_orig / h_dsg > 2) & (acc_dsg - acc_orig > 0.07)
return self.assertTrue(result)
def test_localized_centering(self):
"""Test whether hubness and k-NN accuracy improve for dexter"""
h_orig = hubness(self.distance)[0]
acc_orig = score(self.distance, self.target)[0][0, 0]
sim_lcent = localized_centering(self.vectors, "cosine", 20, 1)
h_lcent = hubness(sim_lcent, metric="similarity")[0]
acc_lcent = score(sim_lcent, self.target, metric="similarity")[0][0, 0]
result = (h_orig / h_lcent > 1.5) & (acc_lcent - acc_orig > 0.03)
return self.assertTrue(result)
def test_ls_dist_equals_sim(self):
"""Test for equal RANKS using dist. vs. sim. (LS_dist != 1-LS_sim).
Using hubness and k-NN accuracy as proxy."""
self.setUpMod('rnd')
ls_dist = local_scaling(self.dist, metric='distance')
ls_sim = local_scaling(1 - self.dist, metric='similarity')
h_dist, _, _ = hubness(ls_dist, metric='distance')
h_sim, _, _ = hubness(ls_sim, metric='similarity')
acc_dist, _, _ = score(ls_dist, self.label, metric='distance')
acc_sim, _, _ = score(ls_sim, self.label, metric='similarity')
dist_sim_equal_in_hubness_knn = np.allclose(h_dist, h_sim) and \
np.allclose(acc_dist, acc_sim)
return self.assertTrue(dist_sim_equal_in_hubness_knn)
def _calc_hubness(self, k:int=5):
"""Calculate hubness (skewness of `k`-occurence).
Also calculate percentage of anti hubs (`k`-occurence == 0) and
percentage of k-NN lists the largest hub occurs in.
"""
S_k, _, N_k = hubness(D=self.secondary_distance,
metric=self.metric, k=k)
self.hubness[k] = S_k
self.anti_hubs[k] = 100 * (N_k == 0).sum() / self.n
self.max_hub_k_occurence[k] = 100 * N_k.max() / self.n
return self
do = 'dexter'
if do == 'random':
print("RANDOM DATA:")
print("------------")
S = triu(rand(1000, 1000, 0.05, 'csr', np.float32, 43), 1)
S += S.T
D = 1. - S.toarray()
elif do == 'dexter':
print("DEXTER:")
print("-------")
D, c, v = load_dexter()
acc_d, _, _ = score(D, c, [5], 'distance')
S = csr_matrix(1 - D)
acc_s, _, _ = score(S, c, [5], 'similarity')
Sn_d, _, _ = hubness(D, 5, 'distance')
Sn_s, _, _ = hubness(S, 5, 'similarity')
print("Orig. dist. hubness:", Sn_d)
print("Orig. sim. hubness:", Sn_s)
if do == 'dexter':
print("Orig. dist. k-NN accuracy:", acc_d)
print('Orig. sim. k-NN accuracy:', acc_s)
D_mp_emp_d = mutual_proximity_empiric(D)
D_mp_emp_s = mutual_proximity_empiric(S, 'similarity')
Sn_mp_emp_d, _, _ = hubness(D_mp_emp_d, 5)
Sn_mp_emp_s, _, _ = hubness(D_mp_emp_s, 5, 'similarity')
print("MP emp dist. hubness:", Sn_mp_emp_d)
print("MP emp sim. hubness:", Sn_mp_emp_s)
if do == 'dexter':
acc_mp_emp_d, _, _ = score(D_mp_emp_d, c, [5], 'distance')
