def test_add_marbles_empty(self):
"""
Corner case: add 0 new Marbles.
"""
marble = generate_dummy_marble()
model = NenwinModel([], set([marble]))
new_marbles = []
model.add_marbles(new_marbles)
expected = set([marble])
self.assertSetEqual(expected, model.marbles)
def test_add_marbles(self):
"""
Base case: add two marbles, should appear in marbles() getter.
Old marbles should remain as well.
"""
marble = generate_dummy_marble()
model = NenwinModel([], set([marble]))
new_marbles = (generate_dummy_marble(), generate_dummy_marble())
model.add_marbles(new_marbles)
expected = set([marble, *new_marbles])
self.assertSetEqual(expected, model.marbles)
self.assertSetEqual(expected, model._NenwinModel__all_particles)
def test_get_params_add_marbles(self):
"""
Later added marbles should not be registered as submodules.
"""
marbles = (generate_dummy_marble(), )
nodes = (generate_dummy_node(), )
model = NenwinModel(nodes)
model.add_marbles(marbles)
# 8 parameters per particle: init_pos, init_vel, init_acc, mass,
# and the 4 stiffness / attraction params
result = tuple(model.parameters())
print(result)
self.assertEqual(len(result), 8)
def test_reset_removed_added_marbles(self):
"""
NenwinModel.reset() should remove all Marbles
added with NenwinModel.add_marbles()
"""
original_marble = Marble(ZERO,
ZERO,
ZERO,
10,
NewtonianGravity(),
datum=None)
added_marble = original_marble.copy()
assert added_marble is not original_marble
model = NenwinModel([], [original_marble])
model.add_marbles([added_marble])
model.reset()
self.assertIn(original_marble, model.marbles)
self.assertIn(original_marble, model._NenwinModel__all_particles)
self.assertNotIn(added_marble, model.marbles)
self.assertNotIn(added_marble, model._NenwinModel__all_particles)
class LossFunctionCallWrongPredTestCase(unittest.TestCase):
"""
Testcases for NenwinLossFunction.__call__() for when another than the target
output-MarbleEaterNodes has eaten a Marble.
"""
def setUp(self):
"""
Sketch:
|
^|
y|
|
0| N_1 <M N_0
|
|
+-------------------------------
-10 0 10 x>
Marble M starts with moving towards N_1, but should arrive at N_0.
"""
self.pos_weight = 0.5
self.vel_weight = 0.5
self.nodes = (gen_node_at(torch.tensor([10.0, 0])),
gen_node_at(torch.tensor([-10.0, 0])))
self.marble = gen_marble_at(ZERO,
vel=torch.tensor([-3.0, 0]),
datum="original")
self.model = NenwinModel(self.nodes, [self.marble])
for _ in range(50): # Should be enough for the Marble to be eaten
self.model.make_timestep(0.1)
assert len(self.model.marbles) == 0, "Testcase badly designed."
self.loss_fun = NenwinLossFunction(self.nodes, self.model,
vel_weight=self.vel_weight,
pos_weight=self.pos_weight)
self.target_index = 0
self.wrong_node_index = 1
self.loss = self.loss_fun(self.target_index)
def test_value_loss_wrong_pred_no_marble_left(self):
"""
The loss should equal the velocity-weighted distance
of the target Node to the nearest Marble,
plus the *negative reciprocal* of the distance of the wrong node
(self.nodes[1]) to the Marble.
Case where no non-eaten Marble is available.
"""
target_node = self.nodes[self.target_index]
wrong_node = self.nodes[self.wrong_node_index]
expected = velocity_weighted_distance(target_node, self.marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
expected -= 1/velocity_weighted_distance(wrong_node, self.marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
torch.testing.assert_allclose(self.loss, expected)
def test_value_loss_wrong_pred_some_marble_left(self):
"""
The loss should equal the velocity-weighted distance
of the target Node to the nearest Marble,
plus the *negative reciprocal* of the distance of the wrong node
(self.nodes[1]) to the Marble.
Case where another non-eaten Marble is still available
at time of loss computation.
"""
second_marble = gen_marble_at(ZERO, datum="second")
self.model.add_marbles([second_marble])
self.loss = self.loss_fun(self.target_index)
target_node = self.nodes[self.target_index]
wrong_node = self.nodes[self.wrong_node_index]
expected = velocity_weighted_distance(target_node, second_marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
expected -= 1/velocity_weighted_distance(wrong_node, self.marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
torch.testing.assert_allclose(self.loss, expected)
def test_grads_no_error_wrong_pred(self):
"""
Case in which no prediction output is given.
All learnable parameters should have a gradient value
that does not cause errors with an optimizer.
Case where another non-eaten Marble is still available
at time of loss computation.
"""
second_marble = gen_marble_at(ZERO)
self.model.add_marbles([second_marble])
optim = torch.optim.Adam(self.model.parameters())
optim.zero_grad()
try:
self.loss.backward()
optim.step()
except RuntimeError as e:
self.fail(f"Error occurred during backprop/optim step: {e}")
def test_grads_value_wrong_pred(self):
"""
Case in which no prediction output is given.
The learnable parameters of the Marble should have a gradient value
that does affect the values of the weights.
The loss should be lower for the second run.
"""
second_marble = gen_marble_at(ZERO)
self.model.add_marbles([second_marble])
optim = torch.optim.Adam(self.model.parameters())
optim.zero_grad()
self.loss.backward()
self.assertIsNotNone(self.marble.init_pos.grad)
self.assertIsNotNone(self.marble.init_vel.grad)
self.assertIsNotNone(self.marble.mass.grad)
optim.step()
# Now verify the loss improved.
self.model.reset()
self.model.make_timestep(0.1)
self.model.add_marbles([second_marble])
new_loss = self.loss_fun(self.target_index)
self.assertLess(new_loss.item(), self.loss.item())
