def test_vote_entropy(self):
for n_samples in range(1, 10):
for n_classes in range(1, 10):
for true_query_idx in range(n_samples):
# 1. fitted committee
vote_return = np.zeros(shape=(n_samples, n_classes),
dtype=np.int16)
vote_return[true_query_idx] = np.asarray(range(n_classes),
dtype=np.int16)
committee = mock.MockCommittee(classes_=np.asarray(
range(n_classes)),
vote_return=vote_return)
vote_entr = modAL.disagreement.vote_entropy(
committee, np.random.rand(n_samples, n_classes))
true_entropy = np.zeros(shape=(n_samples, ))
true_entropy[true_query_idx] = entropy(
np.ones(n_classes) / n_classes)
np.testing.assert_array_almost_equal(
vote_entr, true_entropy)
# 2. unfitted committee
committee = mock.MockCommittee(fitted=False)
true_entropy = np.zeros(shape=(n_samples, ))
vote_entr = modAL.disagreement.vote_entropy(
committee, np.random.rand(n_samples, n_classes))
np.testing.assert_almost_equal(vote_entr, true_entropy)
def test_KL_max_disagreement(self):
for n_samples in range(1, 10):
for n_classes in range(2, 10):
for n_learners in range(2, 10):
# 1. fitted committee
vote_proba = np.zeros(shape=(n_samples, n_learners,
n_classes))
vote_proba[:, :, 0] = 1.0
committee = mock.MockCommittee(
n_learners=n_learners,
classes_=range(n_classes),
vote_proba_return=vote_proba)
true_KL_disagreement = np.zeros(shape=(n_samples, ))
try:
np.testing.assert_array_almost_equal(
true_KL_disagreement,
modAL.disagreement.KL_max_disagreement(
committee, np.random.rand(n_samples, 1)))
except:
modAL.disagreement.KL_max_disagreement(
committee, np.random.rand(n_samples, 1))
# 2. unfitted committee
committee = mock.MockCommittee(fitted=False)
true_KL_disagreement = np.zeros(shape=(n_samples, ))
returned_KL_disagreement = modAL.disagreement.KL_max_disagreement(
committee, np.random.rand(n_samples, n_classes))
np.testing.assert_almost_equal(returned_KL_disagreement,
true_KL_disagreement)
def test_consensus_entropy(self):
for n_samples in range(1, 10):
for n_classes in range(2, 10):
for true_query_idx in range(n_samples):
# 1. fitted committee
proba = np.zeros(shape=(n_samples, n_classes))
proba[:, 0] = 1.0
proba[true_query_idx] = np.ones(n_classes)/n_classes
committee = mock.MockCommittee(predict_proba_return=proba)
consensus_entropy = modAL.disagreement.consensus_entropy(
committee, np.random.rand(n_samples, n_classes)
)
true_entropy = np.zeros(shape=(n_samples,))
true_entropy[true_query_idx] = entropy(np.ones(n_classes) / n_classes)
np.testing.assert_array_almost_equal(consensus_entropy, true_entropy)
# 2. unfitted committee
committee = mock.MockCommittee(fitted=False)
true_entropy = np.zeros(shape=(n_samples,))
consensus_entropy = modAL.disagreement.consensus_entropy(
committee, np.random.rand(n_samples, n_classes)
)
np.testing.assert_almost_equal(consensus_entropy, true_entropy)