def testNotEqualOtherClass(self):
spec_1 = array_spec.ArraySpec((1, 2, 3), np.int32)
spec_2 = None
self.assertNotEqual(spec_1, spec_2)
self.assertNotEqual(spec_2, spec_1)
spec_2 = ()
self.assertNotEqual(spec_1, spec_2)
self.assertNotEqual(spec_2, spec_1)
def _compute_observation_spec(self):
"""Helper for `__init__`: compute our environment's observation spec."""
# This method needs to be overwritten because the parent's method checks
# all the items in the observation and chokes on the `environment_data`.
# Start an environment, examine the values it gives to us, and reset things
# back to default.
timestep = self.reset()
observation_spec = {k: specs.ArraySpec(v.shape, v.dtype, name=k)
for k, v in six.iteritems(timestep.observation)
if k != EXTRA_OBSERVATIONS}
observation_spec[EXTRA_OBSERVATIONS] = dict()
self._drop_last_episode()
return observation_spec
def testNotEqualOtherClass(self):
spec_1 = array_spec.BoundedArraySpec((1, 2),
np.int32,
minimum=[0.0, -0.6],
maximum=[1.0, 1.0])
spec_2 = array_spec.ArraySpec((1, 2), np.int32)
self.assertNotEqual(spec_1, spec_2)
self.assertNotEqual(spec_2, spec_1)
spec_2 = None
self.assertNotEqual(spec_1, spec_2)
self.assertNotEqual(spec_2, spec_1)
spec_2 = ()
self.assertNotEqual(spec_1, spec_2)
self.assertNotEqual(spec_2, spec_1)
def _compute_observation_spec(self):
"""Helper for `__init__`: compute our environment's observation spec."""
# Start an environment, examine the values it gives to us, and reset things
# back to default.
timestep = self.reset()
observation_spec = {k: specs.ArraySpec(v.shape, v.dtype, name=k)
for k, v in six.iteritems(timestep.observation)}
# As long as we've got environment result data, we try checking to make sure
# that the reward types can be added together---a very weak way of measuring
# whether they are compatible.
if timestep.reward is not None:
try:
_ = timestep.reward + self._default_reward
except TypeError:
raise TypeError(
'A pycolab game wrapped by an Environment adapter returned '
'a first reward whose type is incompatible with the default reward '
"given to the adapter's `__init__`.")
self._drop_last_episode()
return observation_spec
def testGenerateValue(self):
spec = array_spec.ArraySpec((1, 2), np.int32)
test_value = spec.generate_value()
spec.validate(test_value)
def testValidateShape(self):
spec = array_spec.ArraySpec((1, 2), np.int32)
spec.validate(np.zeros((1, 2), dtype=np.int32))
with self.assertRaises(ValueError):
spec.validate(np.zeros((1, 2, 3), dtype=np.int32))
def testNotEqualDifferentDtype(self):
spec_1 = array_spec.ArraySpec((1, 2, 3), np.int64)
spec_2 = array_spec.ArraySpec((1, 2, 3), np.int32)
self.assertNotEqual(spec_1, spec_2)
def testEqual(self):
spec_1 = array_spec.ArraySpec((1, 2, 3), np.int32)
spec_2 = array_spec.ArraySpec((1, 2, 3), np.int32)
self.assertEqual(spec_1, spec_2)
def testShape(self):
spec = array_spec.ArraySpec([1, 2, 3], np.int32)
self.assertEqual((1, 2, 3), spec.shape)
def testDtype(self):
spec = array_spec.ArraySpec((1, 2, 3), np.int32)
self.assertEqual(np.int32, spec.dtype)
def testNumpyDtype(self):
array_spec.ArraySpec((1, 2, 3), np.int32)
def testStringDtype(self):
array_spec.ArraySpec((1, 2, 3), "int32")
def testDtypeTypeError(self):
with self.assertRaises(TypeError):
array_spec.ArraySpec((1, 2, 3), "32")
def testShapeTypeError(self):
with self.assertRaises(TypeError):
array_spec.ArraySpec(32, np.int32)
