def test_predict_mocked(self, rf_mock):
class SideEffect(object):
def __init__(self):
self.counter = 0
def __call__(self, X):
self.counter += 1
# Mean and variance
rval = np.array([self.counter] * X.shape[0])
return rval, rval
rf_mock.side_effect = SideEffect()
rs = np.random.RandomState(1)
X = rs.rand(20, 10)
Y = rs.rand(10, 3)
model = UncorrelatedMultiObjectiveRandomForestWithInstances(
['cost', 'ln(runtime)', 'foo'], np.zeros((10, ), dtype=np.uint))
model.train(X[:10], Y[:10])
m_hat, v_hat = model.predict(X[10:])
self.assertEqual(m_hat.shape, (10, 3))
self.assertEqual(v_hat.shape, (10, 3))
self.assertEqual(rf_mock.call_count, 3)
for i in range(10):
for j in range(3):
self.assertEqual(m_hat[i][j], j + 1)
self.assertEqual(v_hat[i][j], j + 1)
def test_train_and_predict_with_rf(self):
rs = np.random.RandomState(1)
X = rs.rand(20, 10)
Y = rs.rand(10, 2)
model = UncorrelatedMultiObjectiveRandomForestWithInstances(
['cost', 'ln(runtime)'], np.zeros((10, ), dtype=np.uint))
model.train(X[:10], Y)
m, v = model.predict(X[10:])
self.assertEqual(m.shape, (10, 2))
self.assertEqual(v.shape, (10, 2))
def test_train_and_predict_with_rf(self):
rs = np.random.RandomState(1)
X = rs.rand(20, 10)
Y = rs.rand(10, 2)
model = UncorrelatedMultiObjectiveRandomForestWithInstances(
['cost', 'ln(runtime)'],
types=np.zeros((10, ), dtype=np.uint),
bounds=np.array([(0, np.nan), (0, np.nan), (0, np.nan),
(0, np.nan), (0, np.nan), (0, np.nan),
(0, np.nan), (0, np.nan), (0, np.nan),
(0, np.nan)],
dtype=object),
rf_kwargs={'seed': 1},
pca_components=5)
self.assertEqual(model.estimators[0].seed, 1)
self.assertEqual(model.estimators[1].seed, 1)
self.assertEqual(model.pca_components, 5)
model.train(X[:10], Y)
m, v = model.predict(X[10:])
self.assertEqual(m.shape, (10, 2))
self.assertEqual(v.shape, (10, 2))