def test_vote(self):
for n_members in range(1, 10):
for n_instances in range(1, 100):
vote_output = np.random.rand(n_instances, n_members)
# assembling the Committee
learner_list = [mock.MockActiveLearner(predict_return=vote_output[:, member_idx])
for member_idx in range(n_members)]
committee = modAL.models.learners.CommitteeRegressor(learner_list=learner_list)
np.testing.assert_array_almost_equal(
committee.vote(np.random.rand(n_instances).reshape(-1, 1)),
vote_output
)
def test_predict(self):
for n_learners in range(1, 10):
for n_instances in range(1, 10):
prediction = np.random.randint(10,
size=(n_instances, n_learners))
committee = modAL.models.Committee(learner_list=[
mock.MockActiveLearner(
mock.MockEstimator(classes_=np.asarray([0])),
predict_return=prediction[:, learner_idx])
for learner_idx in range(n_learners)
])
np.testing.assert_equal(
committee.vote(np.random.rand(n_instances, 5)), prediction)
def test_vote_proba(self):
for n_samples in range(1, 100):
for n_learners in range(1, 10):
for n_classes in range(1, 10):
vote_proba_output = np.random.rand(n_samples, n_learners, n_classes)
# assembling the mock learners
learner_list = [mock.MockActiveLearner(
predict_proba_return=vote_proba_output[:, learner_idx, :],
predictor=mock.MockEstimator(classes_=list(range(n_classes)))
) for learner_idx in range(n_learners)]
committee = modAL.models.learners.Committee(learner_list=learner_list)
np.testing.assert_almost_equal(
committee.vote_proba(np.random.rand(n_samples, 1)),
vote_proba_output
)
def test_vote(self):
for n_members in range(1, 10):
for n_instances in range(1, 100):
vote_output = np.random.randint(0, 2, size=(n_instances, n_members))
# assembling the Committee
learner_list = [mock.MockActiveLearner(
predict_return=vote_output[:, member_idx],
predictor=mock.MockClassifier(classes_=[0])
)
for member_idx in range(n_members)]
committee = modAL.models.Committee(learner_list=learner_list)
np.testing.assert_array_almost_equal(
committee.vote(np.random.rand(n_instances).reshape(-1, 1)),
vote_output
)
def test_predict(self):
for n_members in range(1, 10):
for n_instances in range(1, 100):
vote = np.random.rand(n_instances, n_members)
# assembling the Committee
learner_list = [mock.MockActiveLearner(predict_return=vote[:, member_idx])
for member_idx in range(n_members)]
committee = modAL.models.learners.CommitteeRegressor(learner_list=learner_list)
np.testing.assert_array_almost_equal(
committee.predict(np.random.rand(n_instances).reshape(-1, 1), return_std=False),
np.mean(vote, axis=1)
)
np.testing.assert_array_almost_equal(
committee.predict(np.random.rand(n_instances).reshape(-1, 1), return_std=True),
(np.mean(vote, axis=1), np.std(vote, axis=1))
)
