class GaussianProcessTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.this_dir = os.path.dirname(os.path.abspath(__file__))
cls.test_dir = tempfile.mkdtemp()
def setUp(self):
self.x_train = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
self.y_train = (self.x_train * np.sin(self.x_train)).ravel()
self.gpr = SKLModel(model=GaussianProcessRegressor())
@classmethod
def tearDownClass(cls):
os.chdir(cls.this_dir)
shutil.rmtree(cls.test_dir)
def test_fit_predict(self):
self.gpr.fit(features=self.x_train, targets=self.y_train)
x_test = np.atleast_2d(np.linspace(0, 9, 1000)).T
y_test = x_test * np.sin(x_test)
y_pred, sigma = self.gpr._predict(x_test, return_std=True)
upper_bound = y_pred + 1.96 * sigma
lower_bound = y_pred - 1.96 * sigma
self.assertTrue(
np.all([
l < y and y < u
for u, y, l in zip(upper_bound, y_test, lower_bound)
]))
class LinearModelTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.x_train = np.random.rand(10, 2)
cls.coef = np.random.rand(2)
cls.intercept = np.random.rand()
cls.y_train = cls.x_train.dot(cls.coef) + cls.intercept
def setUp(self):
self.lm = SKLModel(model=LinearRegression())
self.test_dir = tempfile.mkdtemp()
def tearDown(self):
# Remove the directory after the test
shutil.rmtree(self.test_dir)
def test_fit_predict(self):
self.lm.fit(features=self.x_train, targets=self.y_train)
x_test = np.random.rand(10, 2)
y_test = x_test.dot(self.coef) + self.intercept
y_pred = self.lm._predict(x_test)
np.testing.assert_array_almost_equal(y_test, y_pred)
np.testing.assert_array_almost_equal(self.coef, self.lm.model.coef_)
self.assertAlmostEqual(self.intercept, self.lm.model.intercept_)
def test_model_save_load(self):
self.lm.fit(features=self.x_train, targets=self.y_train)
with ScratchDir('.'):
self.lm.save('test_lm.save')
ori = self.lm.model.coef_
self.lm.load('test_lm.save')
loaded = self.lm.model.coef_
np.testing.assert_almost_equal(ori, loaded)
lm2 = SKLModel.from_file('test_lm.save')
np.testing.assert_almost_equal(lm2.model.coef_, ori)
def test_model_none(self):
m = SKLModel(model=LinearRegression())
x = np.array([[1, 2], [2, 1], [1, 1]])
y = np.array([[3], [3], [2]])
m.train(x, y)
np.testing.assert_almost_equal(m.model.coef_.ravel(),
np.array([1.0, 1.0]))
