def test_min_inquiries(self):
"""Test min inquiries criteria."""
alphabet = ['a', 'b', 'c', 'd']
stopping_criteria = CriteriaEvaluator(
continue_criteria=[MinIterationsCriteria(min_num_inq=2)],
commit_criteria=[
MaxIterationsCriteria(max_num_inq=10),
ProbThresholdCriteria(threshold=0.8)
])
stimuli_agent = NBestStimuliAgent(alphabet=alphabet, len_query=2)
decision_maker = mf.DecisionMaker(stimuli_agent=stimuli_agent,
stopping_evaluator=stopping_criteria,
state='',
alphabet=['a', 'b', 'c', 'd'])
# Initialize with initial (language model) probabilities for each letter.
lm_prior = np.array([0.1, 0.1, 0.1, 0.1])
decision_maker.decide(lm_prior)
likelihood = np.array([0.05, 0.05, 0.09, 0.81])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision, "Not enough inquiries presented")
likelihood = np.array([0.05, 0.05, 0.09, 0.81])
decision, _arg = decision_maker.decide(likelihood)
self.assertTrue(decision)
self.assertEqual('d', decision_maker.displayed_state)
def test_decision_maker(self):
"""Test default behavior"""
alphabet = ['a', 'b', 'c', 'd']
stopping_criteria = CriteriaEvaluator(
continue_criteria=[MinIterationsCriteria(min_num_inq=1)],
commit_criteria=[
MaxIterationsCriteria(max_num_inq=10),
ProbThresholdCriteria(threshold=0.8)
])
stimuli_agent = NBestStimuliAgent(alphabet=alphabet, len_query=2)
decision_maker = mf.DecisionMaker(stimuli_agent=stimuli_agent,
stopping_evaluator=stopping_criteria,
state='',
alphabet=['a', 'b', 'c', 'd'])
likelihood = np.array([0.2, 0.2, 0.2, 0.4])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision)
self.assertEqual('.', decision_maker.state)
self.assertEqual('', decision_maker.displayed_state)
likelihood = np.array([0.05, 0.05, 0.1, 0.8])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision, "Must exceed threshold")
self.assertEqual('..', decision_maker.state)
likelihood = np.array([0.05, 0.05, 0.09, 0.81])
decision, _arg = decision_maker.decide(likelihood)
self.assertTrue(decision)
self.assertEqual('..d', decision_maker.state)
self.assertEqual('d', decision_maker.displayed_state)
def test_decision_maker_threshold(self):
"""Threshold should be configurable"""
decision_maker = mf.DecisionMaker(min_num_seq=1,
max_num_seq=10,
state='',
alphabet=['a', 'b', 'c', 'd'],
decision_threshold=0.5)
likelihood = np.array([0.2, 0.6, 0.1, 0.1])
decision, _arg = decision_maker.decide(likelihood)
self.assertTrue(decision, "Value should be above configured threshold")
self.assertEqual('b', decision_maker.displayed_state)
def test_displayed_state(self):
"""Test displayed state"""
decision_maker = mf.DecisionMaker(min_num_seq=1,
max_num_seq=10,
state='ab',
alphabet=['a', 'b', 'c', 'd'],
decision_threshold=0.8)
likelihood = np.array([0.025, 0.025, 0.9, 0.05])
_decision, _arg = decision_maker.decide(likelihood)
self.assertEqual('abc', decision_maker.displayed_state)
def test_min_sequences(self):
"""Test min sequences criteria."""
decision_maker = mf.DecisionMaker(min_num_seq=2,
max_num_seq=10,
state='',
alphabet=['a', 'b', 'c', 'd'],
decision_threshold=0.8)
likelihood = np.array([0.05, 0.05, 0.09, 0.81])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision, "Not enough sequences presented")
likelihood = np.array([0.05, 0.05, 0.09, 0.81])
decision, _arg = decision_maker.decide(likelihood)
self.assertTrue(decision)
self.assertEqual('.d', decision_maker.state)
def test_max_inquiries(self):
"""Test max inquiries criteria."""
alphabet = ['a', 'b', 'c', 'd']
stopping_criteria = CriteriaEvaluator(
continue_criteria=[MinIterationsCriteria(min_num_inq=1)],
commit_criteria=[
MaxIterationsCriteria(max_num_inq=3),
ProbThresholdCriteria(threshold=0.8)
])
stimuli_agent = NBestStimuliAgent(alphabet=alphabet, len_query=2)
decision_maker = mf.DecisionMaker(stimuli_agent=stimuli_agent,
stopping_evaluator=stopping_criteria,
state='',
alphabet=['a', 'b', 'c', 'd'])
# Initialize with initial (language model) probabilities for each letter.
lm_prior = np.array([0.1, 0.1, 0.1, 0.1])
decision_maker.decide(lm_prior)
likelihood = np.array([0.2, 0.2, 0.4, 0.2])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision)
likelihood = np.array([0.2, 0.2, 0.4, 0.2])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision)
likelihood = np.array([0.2, 0.2, 0.4, 0.2])
decision, _arg = decision_maker.decide(likelihood)
self.assertTrue(
decision,
"Should make a decision after max inquiries are reached even if max value is below threshold"
)
self.assertEqual('c', decision_maker.displayed_state)
def test_max_sequences(self):
"""Test max sequences criteria."""
decision_maker = mf.DecisionMaker(min_num_seq=1,
max_num_seq=3,
state='',
alphabet=['a', 'b', 'c', 'd'],
decision_threshold=0.8)
likelihood = np.array([0.2, 0.2, 0.4, 0.2])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision)
likelihood = np.array([0.2, 0.2, 0.4, 0.2])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision)
likelihood = np.array([0.2, 0.2, 0.4, 0.2])
decision, _arg = decision_maker.decide(likelihood)
self.assertTrue(
decision,
"Should make a decision after max sequences are reached even if max value is below threshold"
)
self.assertEqual('c', decision_maker.displayed_state)
def test_decision_maker(self):
"""Test default behavior"""
decision_maker = mf.DecisionMaker(min_num_seq=1,
max_num_seq=10,
state='',
alphabet=['a', 'b', 'c', 'd'],
decision_threshold=0.8)
likelihood = np.array([0.2, 0.2, 0.2, 0.4])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision)
self.assertEqual('.', decision_maker.state)
self.assertEqual('', decision_maker.displayed_state)
likelihood = np.array([0.05, 0.05, 0.1, 0.8])
decision, _arg = decision_maker.decide(likelihood)
self.assertFalse(decision, "Must exceed threshold")
self.assertEqual('..', decision_maker.state)
likelihood = np.array([0.05, 0.05, 0.09, 0.81])
decision, _arg = decision_maker.decide(likelihood)
self.assertTrue(decision)
self.assertEqual('..d', decision_maker.state)
self.assertEqual('d', decision_maker.displayed_state)
def test_decision_maker_threshold(self):
"""Threshold should be configurable"""
alphabet = ['a', 'b', 'c', 'd']
stopping_criteria = CriteriaEvaluator(
continue_criteria=[MinIterationsCriteria(min_num_inq=1)],
commit_criteria=[
MaxIterationsCriteria(max_num_inq=10),
ProbThresholdCriteria(threshold=0.5)
])
stimuli_agent = NBestStimuliAgent(alphabet=alphabet, len_query=2)
decision_maker = mf.DecisionMaker(stimuli_agent=stimuli_agent,
stopping_evaluator=stopping_criteria,
state='',
alphabet=['a', 'b', 'c', 'd'])
# Initialize with initial (language model) probabilities for each letter.
lm_prior = np.array([0.1, 0.1, 0.1, 0.1])
decision_maker.decide(lm_prior)
likelihood = np.array([0.2, 0.6, 0.1, 0.1])
decision, _arg = decision_maker.decide(likelihood)
self.assertTrue(decision, "Value should be above configured threshold")
self.assertEqual('b', decision_maker.displayed_state)