def test_fit_regressor(data):
truth = [0.43418396, 0.43633812, 0.52360168, 0.49169045, 0.4694159]
ctr = CTRegressor(random_state=10)
ctr.fit(data['random_data'], data['random_labels'])
pred = (ctr.predict(data['random_test'])).tolist()
assert len(truth) == len(pred)
for i, j in zip(truth, pred):
assert abs(i-j) < 0.0000001
def test_set_k_neighbors_less_than_labelled_size():
X1 = [[0], [1], [2], [3], [4], [5], [6]]
X2 = [[2], [3], [4], [6], [7], [8], [10]]
y = [10, 11, 12, 13, 14, 15, 16]
y_train = [10, np.nan, 12, np.nan, 14, np.nan, 16]
ctr = CTRegressor(k_neighbors=5, random_state=42)
with pytest.raises(ValueError):
ctr.fit([X1, X2], y_train)
def test_set_unlabeled_pool_size_as_ten(data):
truth = [0.48274064, 0.43065151, 0.45900958, 0.50178144, 0.49710094]
unlabeled_pool_size = 10
ctr = CTRegressor(unlabeled_pool_size=unlabeled_pool_size, random_state=10)
ctr.fit(data['random_data'], data['random_labels'])
pred = (ctr.predict(data['random_test'])).tolist()
assert ctr.unlabeled_pool_size == unlabeled_pool_size
assert len(truth) == len(pred)
for i, j in zip(truth, pred):
assert abs(i-j) < 0.00000001
def test_set_unlabeled_pool_size_as_two(data):
truth = [0.45287164, 0.44940644, 0.45653891, 0.48608956, 0.53500596]
unlabeled_pool_size = 2
ctr = CTRegressor(unlabeled_pool_size=unlabeled_pool_size, random_state=10)
ctr.fit(data['random_data'], data['random_labels'])
pred = (ctr.predict(data['random_test'])).tolist()
assert ctr.unlabeled_pool_size == unlabeled_pool_size
assert len(truth) == len(pred)
for i, j in zip(truth, pred):
assert abs(i-j) < 0.00000001
def test_set_unlabeled_pool_size_as_one(data):
truth = [0.39130458, 0.48810745, 0.45749297, 0.48121387, 0.49026534]
unlabeled_pool_size = 1
ctr = CTRegressor(unlabeled_pool_size=unlabeled_pool_size, random_state=10)
ctr.fit(data['random_data'], data['random_labels'])
pred = (ctr.predict(data['random_test'])).tolist()
assert ctr.unlabeled_pool_size == unlabeled_pool_size
assert len(truth) == len(pred)
for i, j in zip(truth, pred):
assert abs(i-j) < 0.00000001
def test_set_k_neighbors(data):
truth = [0.48113792, 0.44530868, 0.5269744, 0.50646773, 0.53355599]
k_neighbors = 4
ctr = CTRegressor(k_neighbors=k_neighbors, random_state=10)
ctr.fit(data['random_data'], data['random_labels'])
pred = (ctr.predict(data['random_test'])).tolist()
assert ctr.k_neighbors_ == k_neighbors
assert len(truth) == len(pred)
for i, j in zip(truth, pred):
assert abs(i-j) < 0.00000001
def test_set_num_iter(data):
truth = [0.34590772, 0.47308874, 0.43699262, 0.48092033, 0.41690168]
num_iter = 10
ctr = CTRegressor(num_iter=num_iter, random_state=10)
ctr.fit(data['random_data'], data['random_labels'])
pred = (ctr.predict(data['random_test'])).tolist()
assert ctr.num_iter == num_iter
assert len(truth) == len(pred)
for i, j in zip(truth, pred):
assert abs(i-j) < 0.00000001
def test_set_n_neighbors_as_one():
X1 = [[0], [1], [2], [3], [4], [5], [6]]
X2 = [[2], [3], [4], [6], [7], [8], [10]]
y = [10, -200, 12, 13, -100, 15, 16]
y_train = [10, np.nan, np.nan, 13, np.nan, 15, 16]
truth = [10.75, 10.75, 12.25, 13.75, 13.75, 14.75, 15.5]
ctr = CTRegressor(
KNeighborsRegressor(n_neighbors=2),
KNeighborsRegressor(n_neighbors=2),
k_neighbors=2, random_state=42)
ctr.fit([X1, X2], y_train)
pred = ctr.predict([X1, X2])
for i, j in zip(truth, pred):
assert abs(i-j) < 0.00000001