def test_partition_05(self):
np.random.seed(42)
matrix = np.random.uniform(-1, 1, 9).reshape(3, 3)
ss = ActiveSubspaces()
ss.evects = matrix
with self.assertRaises(ValueError):
ss.partition(dim=4)
def test_partition_02(self):
np.random.seed(42)
matrix = np.random.uniform(-1, 1, 9).reshape(3, 3)
ss = ActiveSubspaces()
ss.evects = matrix
ss.partition(dim=2)
np.testing.assert_array_almost_equal(matrix[:, 2:], ss.W2)
def test_plot_sufficient_summary_02(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 200).reshape(50, 4)
weights = np.ones((50, 1)) / 50
ss = ActiveSubspaces()
ss.compute(gradients=gradients, weights=weights, nboot=200)
ss.partition(3)
with self.assertRaises(ValueError):
ss.plot_sufficient_summary(10, 10)
def test_forward_01(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 60).reshape(15, 4)
outputs = np.random.uniform(0, 5, 15)
ss = ActiveSubspaces()
ss.compute(inputs=inputs, outputs=outputs, method='local', nboot=250)
ss.partition(2)
active = ss.forward(np.random.uniform(-1, 1, 8).reshape(2, 4))[0]
true_active = np.array([[-0.004748, 0.331107], [-0.949099, 0.347534]])
np.testing.assert_array_almost_equal(true_active, active)
def test_plot_sufficient_summary_03(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 200).reshape(50, 4)
weights = np.ones((50, 1)) / 50
ss = ActiveSubspaces()
ss.compute(gradients=gradients, weights=weights, nboot=200)
ss.partition(2)
with assert_plot_figures_added():
ss.plot_sufficient_summary(
np.random.uniform(-1, 1, 100).reshape(25, 4),
np.random.uniform(-1, 1, 25).reshape(-1, 1))
def test_forward_05(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 60).reshape(15, 4)
outputs = np.random.uniform(0, 5, 15)
ss = ActiveSubspaces()
ss.compute(inputs=inputs, outputs=outputs, method='local', nboot=250)
ss.partition(2)
inactive = ss.forward(np.random.uniform(-1, 1, 8).reshape(2, 4))[1]
true_inactive = np.array([[-1.03574242, -0.04662904],
[-0.49850367, -0.37146678]])
np.testing.assert_array_almost_equal(true_inactive, inactive)
def test_forward_04(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 60).reshape(15, 4)
outputs = np.random.uniform(0, 5, 45).reshape(15, 3)
ss = ActiveSubspaces()
ss.compute(inputs=inputs, outputs=outputs, method='local', nboot=250)
ss.partition(2)
active = ss.forward(np.random.uniform(-1, 1, 8).reshape(2, 4))[0]
true_active = np.array([[0.15284753, 0.67109407],
[0.69006622, -0.4165206]])
np.testing.assert_array_almost_equal(true_active, active)
def test_backward_02(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 80).reshape(16, 5)
outputs = np.random.uniform(-1, 3, 16)
ss = ActiveSubspaces()
ss.compute(inputs=inputs, outputs=outputs, method='local', nboot=250)
ss.partition(2)
new_inputs = np.random.uniform(-1, 1, 15).reshape(3, 5)
active = ss.forward(new_inputs)[0]
new_inputs = ss.backward(reduced_inputs=active, n_points=500)[0]
np.testing.assert_array_almost_equal(
np.kron(active, np.ones((500, 1))), new_inputs.dot(ss.W1))
def test_forward_03(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 60).reshape(15, 4)
outputs = np.random.uniform(0, 5, 45).reshape(15, 3)
ss = ActiveSubspaces()
ss.compute(inputs=inputs,
outputs=outputs,
method='local',
nboot=250,
metric=np.diag(np.ones(3)))
ss.partition(2)
new_inputs = np.random.uniform(-1, 1, 8).reshape(2, 4)
active, inactive = ss.forward(new_inputs)
reconstructed_inputs = active.dot(ss.W1.T) + inactive.dot(ss.W2.T)
np.testing.assert_array_almost_equal(new_inputs, reconstructed_inputs)
def test_hit_and_run_inactive_01(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 60).reshape(15, 4)
outputs = np.random.uniform(0, 5, 15)
ss = ActiveSubspaces()
ss.compute(inputs=inputs, outputs=outputs, method='local', nboot=250)
ss.partition(1)
new_inputs = np.random.uniform(-1, 1, 8).reshape(2, 4)
active = ss.forward(new_inputs)[0]
inactive_swap = np.array([
ss._hit_and_run_inactive(reduced_input=red_inp, n_points=1)
for red_inp in active
])
inactive_inputs = np.swapaxes(inactive_swap, 1, 2)
new_inputs = ss._rotate_x(reduced_inputs=active,
inactive_inputs=inactive_inputs)[0]
np.testing.assert_array_almost_equal(active, new_inputs.dot(ss.W1))