def test_chemistries():
"""Return a subset of chemistries to test conversion."""
chems = [
utils.Chemistry(potion_map=stones_and_potions.all_fixed_potion_map(),
stone_map=stones_and_potions.all_fixed_stone_map(),
graph=graphs.create_graph_from_constraint(
graphs.no_bottleneck_constraints()[0]),
rotation=np.eye(3)),
utils.Chemistry(potion_map=stones_and_potions.PotionMap([1, 0, 2],
[1, 1, -1]),
stone_map=stones_and_potions.StoneMap(
np.array([-1, 1, -1])),
graph=graphs.create_graph_from_constraint(
graphs.bottleneck1_constraints()[0]),
rotation=stones_and_potions.possible_rotations()[-1])
]
for r in stones_and_potions.possible_rotations():
for sm in stones_and_potions.possible_stone_maps():
chems.append(
utils.Chemistry(
potion_map=stones_and_potions.all_fixed_potion_map(),
stone_map=sm,
graph=graphs.create_graph_from_constraint(
graphs.no_bottleneck_constraints()[0]),
rotation=r))
return chems
def make_fixed_chem_env(
constraint=None, potion_map=_FIXED_POTION_MAP, stone_map=_FIXED_STONE_MAP,
rotation=_FIXED_ROTATION, test_stones=None, test_potions=None, **kwargs):
if constraint is None:
constraint = graphs.no_bottleneck_constraints()[0]
env = symbolic_alchemy.get_symbolic_alchemy_fixed(
episode_items=utils.EpisodeItems(
potions=test_potions or _TEST_POTIONS,
stones=test_stones or _TEST_STONES),
chemistry=utils.Chemistry(
graph=graphs.create_graph_from_constraint(constraint),
potion_map=potion_map, stone_map=stone_map, rotation=rotation),
reward_weights=reward_fcn(), max_steps_per_trial=_MAX_STEPS_PER_TRIAL,
**kwargs)
return env
def test_see_chem_before_reset(self):
env = self._make_env(
see_chemistry=utils.ChemistrySeen(
content=utils.ElementContent.GROUND_TRUTH),
constraint=graphs.no_bottleneck_constraints()[0])
obs = env.observation()
# Observation should be all unknown because we have not reset the
# environment yet.
np.testing.assert_allclose(obs[_CHEM_NAME], [0.5] * 28)
# After resetting none of the chem should be unknown.
env.reset()
obs = env.observation()
np.testing.assert_array_less(
0.01 * np.ones((28,)),
np.abs(obs[_CHEM_NAME] - np.array([0.5] * 28)))
def get_potion_tests():
"""Test cases for converting between potions and unity potion properties."""
potion_tests = []
for pm in stones_and_potions.possible_potion_maps(
precomputed_maps.get_perm_index_conversion()[1]):
potion_tests.append((
[(pm.apply_inverse(l), l)
for l in stones_and_potions.possible_latent_potions()],
functools.partial(
unity_python_conversion.to_potion_unity_properties,
# It shouldn't matter what graph we use for testing this part.
graph=graphs.create_graph_from_constraint(
graphs.no_bottleneck_constraints()[0])),
unity_python_conversion._potions_from_potion_unity_properties,
lambda x: x,
_make_tuple))
return potion_tests
def setUp(self):
super().setUp()
self.env3d_mock = Mock3DEnv()
self.chemistry = utils.Chemistry(
potion_map=stones_and_potions.all_fixed_potion_map(),
stone_map=stones_and_potions.all_fixed_stone_map(),
graph=graphs.create_graph_from_constraint(
graphs.no_bottleneck_constraints()[0]),
rotation=np.eye(3))
self.items = utils.EpisodeItems(potions=[[Potion(0, 0, 1)],
[Potion(1, 2, -1)]],
stones=[[Stone(2, [-1, -1, -1])],
[Stone(3, [1, 1, 1])]])
self.env3d_mock.set_chemistry_and_items(self.chemistry, self.items)
self.wrapper = symbolic_alchemy_wrapper.SymbolicAlchemyWrapper(
self.env3d_mock,
'alchemy/perceptual_mapping_randomized_with_random_bottleneck')
Counter = collections.Counter
AlignedStone = stones_and_potions.AlignedStone
PerceivedPotion = stones_and_potions.PerceivedPotion
PotionMap = stones_and_potions.PotionMap
StoneMap = stones_and_potions.StoneMap
Stone = stones_and_potions.Stone
Potion = stones_and_potions.Potion
AddMatrixEventTracker = symbolic_alchemy_trackers.AddMatrixEventTracker
ScoreTracker = symbolic_alchemy_trackers.ScoreTracker
BeliefStateTracker = symbolic_alchemy_trackers.BeliefStateTracker
_ALL_FIXED_POTION_MAP = stones_and_potions.all_fixed_potion_map()
_ALL_FIXED_STONE_MAP = stones_and_potions.all_fixed_stone_map()
_ALL_FIXED_GRAPH = graphs.create_graph_from_constraint(
graphs.no_bottleneck_constraints()[0])
_BOTTLENECK1_CONSTRAINT = graphs.bottleneck1_constraints()[0]
_BOTTLENECK1_GRAPH = graphs.create_graph_from_constraint(
_BOTTLENECK1_CONSTRAINT)
def add_trackers_to_env(env, reward_weights, precomputed, init_belief_state):
env.add_trackers({
AddMatrixEventTracker.NAME:
AddMatrixEventTracker(),
ScoreTracker.NAME:
ScoreTracker(reward_weights),
BeliefStateTracker.NAME:
BeliefStateTracker(precomputed, env, init_belief_state)
})
class SymbolicAlchemySeeChemistryTest(parameterized.TestCase):
"""We don't do the full mixin tests for the chemistry observation."""
def _make_env(self, see_chemistry, constraint, **kwargs):
return make_fixed_chem_env(
constraint=constraint,
see_chemistries={_CHEM_NAME: see_chemistry},
observe_used=True, end_trial_action=False, **kwargs)
@parameterized.parameters(
# In the graph observations edges are in the following order:
# _________11__________
# /| /|
# 9/ | 10/ |
# / | / |
# /___|_____8_________/ |
# | |6 | |7
# | | | |
# |2 | |4 |
# | |_______5_______|___|
# | / | /
# | /1 | /3
# | / | /
# |/________0_________|/
#
# With coordinate system:
# |
# |z /
# | /y
# | /
# |/___x___
#
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
rotation=utils.RotationElement(present=False),
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.no_bottleneck_constraints()[0],
# With no constraints all edges should be present
'expected_obs': np.ones((12,), np.float32),
'expected_len': 12},
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
rotation=utils.RotationElement(present=False),
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.bottleneck1_constraints()[0],
# For bottleneck1 constraint the only x direction edge that exists is 8,
# so 0, 5 and 11 are missing.
'expected_obs': np.array([0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0,
1.0, 1.0, 0.0], np.float32),
'expected_len': 12},
{'see_chemistry': utils.ChemistrySeen(
stone_map=utils.StoneMapElement(present=False),
graph=utils.GraphElement(present=False),
rotation=utils.RotationElement(present=False),
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.no_bottleneck_constraints()[0],
# First 6 entries are a 1-hot for the dimension map, in this case the
# dimension map used is the first one.
# The next 3 entries are 0 or 1 for the direction map, in this case all
# directions are positive.
'expected_obs': np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
np.float32),
'expected_len': 9},
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
graph=utils.GraphElement(present=False),
rotation=utils.RotationElement(present=False),
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.no_bottleneck_constraints()[0],
# 3 entries are 0 or 1 for the direction map, in this case all directions
# are positive.
'expected_obs': np.array([1.0, 1.0, 1.0], np.float32),
'expected_len': 3},
{'see_chemistry': utils.ChemistrySeen(
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.no_bottleneck_constraints()[0],
# Observations are from the previous tests concatenated with graph first,
# then potion map then stone map.
'expected_obs': np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, # graph
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, # graph
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, # potion dim map
1.0, 1.0, 1.0, # potion dir map
1.0, 1.0, 1.0, # stone map
1.0, 0.0, 0.0, 0.0], np.float32), # rotation
'expected_len': 28},
# Tests for the belief state observation.
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
rotation=utils.RotationElement(present=False),
content=utils.ElementContent.BELIEF_STATE,
precomputed='perceptual_mapping_randomized_with_random_bottleneck'),
'constraint': graphs.no_bottleneck_constraints()[0],
# With no actions the belief state should be unknown for all edges.
'expected_obs': 0.5 * np.ones((12,), np.float32),
'expected_len': 12},
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
rotation=utils.RotationElement(present=False),
content=utils.ElementContent.BELIEF_STATE,
precomputed='perceptual_mapping_randomized_with_random_bottleneck'),
'constraint': graphs.bottleneck1_constraints()[0],
# It shouldn't make a difference whether the underlying chemistry has a
# constraint or not everythin is unknown.
'expected_obs': 0.5 * np.ones((12,), np.float32),
'expected_len': 12},
{'see_chemistry': utils.ChemistrySeen(
stone_map=utils.StoneMapElement(present=False),
graph=utils.GraphElement(present=False),
rotation=utils.RotationElement(present=False),
content=utils.ElementContent.BELIEF_STATE,
precomputed='perceptual_mapping_randomized_with_random_bottleneck'),
'constraint': graphs.no_bottleneck_constraints()[0],
# First 6 entries are a 1-hot for the dimension map, with no actions all
# of the dimesnsion maps are possible so the entries are all unknown.
# The next 3 entries are 0 or 1 for the direction map, or 0.5 for unknown
# which is the case if no actions are taken.
'expected_obs': 0.5 * np.ones((9,), np.float32),
'expected_len': 9},
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
graph=utils.GraphElement(present=False),
rotation=utils.RotationElement(present=False),
content=utils.ElementContent.BELIEF_STATE,
precomputed='perceptual_mapping_randomized_with_random_bottleneck'),
'constraint': graphs.no_bottleneck_constraints()[0],
# 3 entries are 0 or 1 for the direction map, in this case all directions
# are positive, since the test stones include an instance of the best
# stone, the stone map should be known from the start.
'expected_obs': np.array([1.0, 1.0, 1.0], np.float32),
'expected_len': 3},
{'see_chemistry': utils.ChemistrySeen(
content=utils.ElementContent.BELIEF_STATE,
rotation=utils.RotationElement(present=False),
precomputed='perceptual_mapping_randomized_with_random_bottleneck'),
'constraint': graphs.no_bottleneck_constraints()[0],
# Observations are from the previous tests concatenated with graph first,
# then potion map then stone map.
'expected_obs': np.array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, # graph
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, # graph
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, # potion dim map
0.5, 0.5, 0.5, # potion dir map
1.0, 1.0, 1.0], np.float32), # stone map
'expected_len': 24},
{'see_chemistry': utils.ChemistrySeen(
content=utils.ElementContent.BELIEF_STATE,
rotation=utils.RotationElement(present=False),
precomputed='perceptual_mapping_randomized_with_random_bottleneck'),
'constraint': graphs.no_bottleneck_constraints()[0],
'actions': [utils.SlotBasedAction(stone_ind=0, potion_ind=0)],
# If we put the 0th stone into the 0th potion we will see a change on
# axis 1, we will become certain that the dim map is either [0, 1, 2] or
# [2, 1, 0], we will become certain that the edge from (-1, -1, 1) to
# (-1, 1, 1) exists.
'expected_obs': np.array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, # graph
0.5, 0.5, 0.5, 1.0, 0.5, 0.5, # graph
0.5, 0.0, 0.0, 0.0, 0.0, 0.5, # potion dim map
0.5, 1.0, 0.5, # potion dir map
1.0, 1.0, 1.0], np.float32), # stone map
'expected_len': 24},
# Tests for a combination of content types
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
rotation=utils.RotationElement(present=False),
groups=[
utils.GroupInChemistry(
{utils.ElementType.GRAPH: {0, 1, 2, 3}}, [1.0, 0.0, 0.0]),
utils.GroupInChemistry(
{utils.ElementType.GRAPH: {4, 5, 6}}, [0.0, 0.0, 1.0]),
utils.GroupInChemistry(
{utils.ElementType.GRAPH: {7, 8, 9, 10, 11}}, [0.0, 1.0, 0.0]),
],
precomputed='perceptual_mapping_randomized_with_random_bottleneck'),
'constraint': graphs.bottleneck1_constraints()[0],
'actions': [utils.SlotBasedAction(stone_ind=0, potion_ind=0)],
# With no actions the belief state should be unknown for all edges.
'expected_obs': np.array(
[0.0, 1.0, 1.0, 1.0, # ground truth - 0 missing 1, 2, 3 exist
0.5, 0.5, 0.5, # unknown - these are set to 0.5
# belief state - after the action 9 is known, others are unknown
0.5, 0.5, 1.0, 0.5, 0.5], np.float32),
'expected_len': 12},
# Rotation tests
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
graph=utils.GraphElement(present=False),
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.no_bottleneck_constraints()[0],
'make_env_kwargs': {
'rotation': stones_and_potions.rotation_from_angles([0, 0, 0])},
'expected_obs': np.array([1.0, 0.0, 0.0, 0.0], np.float32),
'expected_len': 4},
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
graph=utils.GraphElement(present=False),
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.no_bottleneck_constraints()[0],
'make_env_kwargs': {
'rotation': stones_and_potions.rotation_from_angles([0, 0, -45])},
'expected_obs': np.array([0.0, 1.0, 0.0, 0.0], np.float32),
'expected_len': 4},
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
graph=utils.GraphElement(present=False),
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.no_bottleneck_constraints()[0],
'make_env_kwargs': {
'rotation': stones_and_potions.rotation_from_angles([0, -45, 0])},
'expected_obs': np.array([0.0, 0.0, 1.0, 0.0], np.float32),
'expected_len': 4},
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
stone_map=utils.StoneMapElement(present=False),
graph=utils.GraphElement(present=False),
content=utils.ElementContent.GROUND_TRUTH),
'constraint': graphs.no_bottleneck_constraints()[0],
'make_env_kwargs': {
'rotation': stones_and_potions.rotation_from_angles([-45, 0, 0])},
'expected_obs': np.array([0.0, 0.0, 0.0, 1.0], np.float32),
'expected_len': 4},
# In belief state if we have stones which are unique to a particular
# rotation then the rotation should be known and possibly part of the
# stone map.
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
graph=utils.GraphElement(present=False),
content=utils.ElementContent.BELIEF_STATE,
precomputed=('perceptual_mapping_randomized_with_rotation_and_'
'random_bottleneck')),
'constraint': graphs.no_bottleneck_constraints()[0],
'make_env_kwargs': {
'rotation': stones_and_potions.rotation_from_angles([-45, 0, 0]),
'test_stones': [[Stone(0, [1, 1, 1]), Stone(0, [1, 1, -1])]]},
'expected_obs': np.array(
[1.0, 1.0, 1.0, # stone map
0.0, 0.0, 0.0, 1.0], np.float32), # rotation
'expected_len': 7},
# Otherwise rotation and stone map observations should both be unknown.
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
graph=utils.GraphElement(present=False),
content=utils.ElementContent.BELIEF_STATE,
precomputed=('perceptual_mapping_randomized_with_rotation_and_'
'random_bottleneck')),
'constraint': graphs.no_bottleneck_constraints()[0],
'make_env_kwargs': {
'rotation': stones_and_potions.rotation_from_angles([-45, 0, 0]),
'test_stones': [[Stone(0, [1, 1, 1])]]},
'expected_obs': np.array(
[0.5, 0.5, 0.5, # stone map
0.5, 0.5, 0.5, 0.5], np.float32), # rotation
'expected_len': 7},
{'see_chemistry': utils.ChemistrySeen(
potion_map=utils.PotionMapElement(present=False),
graph=utils.GraphElement(present=False),
content=utils.ElementContent.BELIEF_STATE,
precomputed=('perceptual_mapping_randomized_with_rotation_and_'
'random_bottleneck')),
'constraint': graphs.no_bottleneck_constraints()[0],
'make_env_kwargs': {
'rotation': stones_and_potions.rotation_from_angles([-45, 0, 0]),
'test_stones': [[Stone(0, [1, 1, 1])]]},
'actions': [utils.SlotBasedAction(stone_ind=0, potion_ind=6)],
'expected_obs': np.array(
[1.0, 1.0, 1.0, # stone map
0.0, 0.0, 0.0, 1.0], np.float32), # rotation
'expected_len': 7},
)
def test_see_chemistry(
self, see_chemistry, constraint, expected_obs, expected_len,
actions=None, make_env_kwargs=None):
"""Test the ground truth chemistry observations."""
env = self._make_env(
see_chemistry=see_chemistry, constraint=constraint,
**(make_env_kwargs or {}))
timestep = env.reset()
if actions:
for action in actions:
timestep = env.step_slot_based_action(action)
np.testing.assert_allclose(
timestep.observation[_CHEM_NAME], expected_obs)
self.assertLen(
timestep.observation[_CHEM_NAME], expected_len)
def test_see_chem_before_reset(self):
env = self._make_env(
see_chemistry=utils.ChemistrySeen(
content=utils.ElementContent.GROUND_TRUTH),
constraint=graphs.no_bottleneck_constraints()[0])
obs = env.observation()
# Observation should be all unknown because we have not reset the
# environment yet.
np.testing.assert_allclose(obs[_CHEM_NAME], [0.5] * 28)
# After resetting none of the chem should be unknown.
env.reset()
obs = env.observation()
np.testing.assert_array_less(
0.01 * np.ones((28,)),
np.abs(obs[_CHEM_NAME] - np.array([0.5] * 28)))