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 test_knn_score_equal_sklearn_loocv_score(self):
acc, correct, cmat = \
score(self.distance, self.label, k=5, metric='distance')
# scoring only one k value, so take just the first elements:
acc = acc[0, 0]
correct = correct[0]
cmat = cmat[0]
# This should work too, but is much slower than using precomp. dist.
#=======================================================================
# knclassifier = KNeighborsClassifier(n_neighbors=5, algorithm='brute',
# metric='cosine')
#=======================================================================
knclassifier = KNeighborsClassifier(n_neighbors=5, algorithm='brute',
metric='precomputed')
n = self.distance.shape[0] # for LOO-CV
loo_cv = LeaveOneOut(n)
predicted_sklearn = cross_val_predict(
knclassifier, self.distance, self.label, cv=loo_cv)
acc_sklearn = accuracy_score(self.label, predicted_sklearn)
if not np.allclose(acc, acc_sklearn):
return self.assertAlmostEqual(acc, acc_sklearn, places=7)
else:
correct_sklearn = predicted_sklearn == self.label
equal_prediction = np.all(correct == correct_sklearn)
msg = """Accuracies of hub toolbox k-NN and sklearn-kNN are almost
equal, but the predictions per data point are not."""
return self.assertTrue(equal_prediction, msg)
def test_knn_score_matches_correct_prediction_fraction(self):
k = np.array([1, 5, 20])
acc, correct, _ = score(self.distance, self.label, k=k)
acc_match = np.zeros_like(k, dtype=bool)
for i, _ in enumerate(k):
cur_acc = acc[i]
cur_correct = correct[i]
acc_match[i] = np.allclose(cur_acc, cur_correct.sum() / self.n)
return self.assertTrue(np.all(acc_match))
def test_knn_score_matches_confusion_matrix(self):
k = np.array([1, 5, 20])
acc, _, cmat = score(self.distance, self.label, k=k)
acc_match = np.zeros_like(k, dtype=bool)
for i, _ in enumerate(k):
cur_acc = acc[i]
cur_cmat = cmat[i]
TP = cur_cmat[0, 0]
FN = cur_cmat[0, 1]
FP = cur_cmat[1, 0]
TN = cur_cmat[1, 1]
acc_from_cmat = (TP + TN) / (TP + FN + FP + TN)
acc_match[i] = np.allclose(cur_acc, acc_from_cmat)
return self.assertTrue(np.all(acc_match))
from hub_toolbox.Hubness import hubness
from hub_toolbox.HubnessAnalysis import load_dexter
from hub_toolbox.KnnClassification import score
#do = 'random'
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')
def _calc_knn_accuracy(self, k:int=5):
"""Calculate `k`-NN accuracy."""
acc, _, _ = score(D=self.secondary_distance, target=self.classes,
k=k, metric=self.metric)
self.knn_accuracy[k] = acc
return self
