def test_get_new_states_probs(self):
state = states.ProductionRulesState(
self._strings_to_production_rules([
'S -> S "+" T',
'S -> T',
]))
# The above production rules sequence are parsed as
# S
# |
# S '+' T
# |
# T
#
# Since the order of the production rules sequence is the preorder traversal
# of the parsing tree, the next symbol to parse is the 'T' on the left side
# of the above parsing tree. Only production rule with left hand side symbol
# T are valid production rule.
# Thus, for grammar with production rules:
# 'S -> S "+" T'
# 'S -> T'
# 'T -> "(" S ")"'
# 'T -> "x"'
# Appending the first two production rules will create invalid state, with
# prior probabilities nan. The last two production rules can be appended
# and will create new states, with equal prior probabilities.
expected_new_states = [
None,
None,
states.ProductionRulesState(
self._strings_to_production_rules([
'S -> S "+" T',
'S -> T',
'T -> "(" S ")"',
])),
states.ProductionRulesState(
self._strings_to_production_rules([
'S -> S "+" T',
'S -> T',
'T -> "x"',
])),
]
policy = policies.ProductionRuleAppendPolicy(grammar=self.grammar)
new_states, action_probs = policy.get_new_states_probs(state)
np.testing.assert_allclose(action_probs, [np.nan, np.nan, 0.5, 0.5])
self.assertEqual(len(new_states), len(expected_new_states))
for new_state, expected_new_state in zip(new_states,
expected_new_states):
self.assertEqual(new_state, expected_new_state)
def test_get_new_states_probs_type_error(self):
policy = policies.ProductionRuleAppendPolicy(grammar=self.grammar)
with self.assertRaisesRegexp(
TypeError, r'Input state shoud be an instance of '
r'states\.ProductionRulesState'):
policy.get_new_states_probs(states.StateBase())