def test_penalization_function_shape():
model = MockModel()
lp = LocalPenalization(model)
lp.update_batches(np.zeros((5, 1)), 1, -0.1)
value = lp.evaluate(np.random.rand(10, 1))
assert value.shape == (10, 1)
def test_local_penalization_gradients_with_multiple_points_in_batch():
np.random.seed(123)
model = MockModel()
lp = LocalPenalization(model)
lp.update_batches(np.random.rand(5, 1), 1, -0.1)
x0 = np.array([0.5])
_check_grad(lp, TOL, x0)
def test_penalization_function_shape():
model = MockModel()
lp = LocalPenalization(model)
lp.update_batches(np.zeros((5, 1)), 1, -0.1)
value = lp.evaluate(np.random.rand(10, 1))
assert value.shape == (10, 1)
def test_local_penalization_gradients_with_multiple_points_in_batch():
np.random.seed(123)
model = MockModel()
lp = LocalPenalization(model)
lp.update_batches(np.random.rand(5, 1), 1, -0.1)
x0 = np.array([0.5])
_check_grad(lp, TOL, x0)
def test_penalization_function_gradients_shape():
model = MockModel()
lp = LocalPenalization(model)
lp.update_batches(np.zeros((5, 2)), 1, -0.1)
val, grad = lp.evaluate_with_gradients(np.random.rand(10, 2))
assert grad.shape == (10, 2)
assert val.shape == (10, 1)
def test_penalization_function_gradients_shape():
model = MockModel()
lp = LocalPenalization(model)
lp.update_batches(np.zeros((5, 2)), 1, -0.1)
val, grad = lp.evaluate_with_gradients(np.random.rand(10, 2))
assert grad.shape == (10, 2)
assert val.shape == (10, 1)
def test_local_penaliztion_at_batch_point():
# Test edge case where evaluating local penalization at a point already in the batch.
# This can lead to divide by zero errors if not done correctly.
np.random.seed(123)
model = MockModel()
lp = LocalPenalization(model)
x_batch = np.random.rand(5, 1)
lp.update_batches(x_batch, 1, -0.1)
val, grad = lp.evaluate_with_gradients(x_batch)
assert not np.any(np.isnan(grad))
def test_local_penalization_at_batch_point():
# Test edge case where evaluating local penalization at a point already in the batch.
# This can lead to divide by zero errors if not done correctly.
np.random.seed(123)
model = MockModel()
lp = LocalPenalization(model)
x_batch = np.random.rand(5, 1)
lp.update_batches(x_batch, 1, -0.1)
val, grad = lp.evaluate_with_gradients(x_batch)
assert not np.any(np.isnan(grad))
