def random_CDMVLP(nb_features, nb_targets, max_feature_values,
max_target_values, algorithm):
dataset = random_StateTransitionsDataset(100, nb_features, nb_targets,
max_feature_values,
max_target_values)
model = CDMVLP(features=dataset.features, targets=dataset.targets)
model.compile(algorithm=algorithm)
model.fit(dataset=dataset)
return model
def test_predict(self):
print(">> CDMVLP.predict()")
for i in range(self._nb_tests):
dataset = random_StateTransitionsDataset( \
nb_transitions=random.randint(1, self._nb_transitions), \
nb_features=random.randint(1,self._nb_features), \
nb_targets=random.randint(1,self._nb_targets), \
max_feature_values=self._nb_feature_values, \
max_target_values=self._nb_target_values)
for algorithm in self._SUPPORTED_ALGORITHMS:
model = CDMVLP(features=dataset.features,
targets=dataset.targets)
model.compile(algorithm=algorithm)
model.fit(dataset=dataset)
feature_states = list(set(
tuple(s1) for s1, s2 in dataset.data))
prediction = model.predict(feature_states)
for state_id, s1 in enumerate(feature_states):
feature_state_encoded = []
for var_id, val in enumerate(s1):
val_id = model.features[var_id][1].index(str(val))
feature_state_encoded.append(val_id)
#eprint(feature_state_encoded)
target_states = SynchronousConstrained.next(
feature_state_encoded, model.targets, model.rules,
model.constraints)
output = []
for s in target_states:
target_state = []
for var_id, val_id in enumerate(s):
#eprint(var_id, val_id)
if val_id == -1:
target_state.append("?")
else:
target_state.append(
model.targets[var_id][1][val_id])
output.append(target_state)
self.assertEqual(prediction[state_id][0], list(s1))
self.assertEqual(prediction[state_id][1], output)
# Force missing value
model.rules = [
r for r in model.rules if
r.head_variable != random.randint(0, len(model.targets))
]
prediction = model.predict(feature_states)
for state_id, s1 in enumerate(feature_states):
feature_state_encoded = []
for var_id, val in enumerate(s1):
val_id = model.features[var_id][1].index(str(val))
feature_state_encoded.append(val_id)
#eprint(feature_state_encoded)
target_states = SynchronousConstrained.next(
feature_state_encoded, model.targets, model.rules,
model.constraints)
output = []
for s in target_states:
target_state = []
for var_id, val_id in enumerate(s):
#eprint(var_id, val_id)
if val_id == -1:
target_state.append("?")
else:
target_state.append(
model.targets[var_id][1][val_id])
output.append(target_state)
self.assertEqual(prediction[state_id][1], output)
# Exceptions:
self.assertRaises(
TypeError, model.predict,
"") # Feature_states bad format: is not a list
self.assertRaises(
TypeError, model.predict,
[["0", "1"], 0, 10
]) # Feature_states bad format: is not a list of list
self.assertRaises(
TypeError, model.predict, [["0", "1"], [0, 10]]
) # Feature_states bad format: is not a list of list of string
feature_states = [
list(s) for s in set(tuple(s1) for s1, s2 in dataset.data)
]
state_id = random.randint(0, len(feature_states) - 1)
original = feature_states[state_id].copy()
feature_states[state_id] = feature_states[
state_id][:-random.randint(1, len(dataset.features))]
self.assertRaises(
TypeError, model.predict, feature_states
) # Feature_states bad format: size of state not correspond to model features <
feature_states[state_id] = original.copy()
feature_states[state_id].extend(
["0" for i in range(random.randint(1, 10))])
self.assertRaises(
TypeError, model.predict, feature_states
) # Feature_states bad format: size of state not correspond to model features >
feature_states[state_id] = original.copy()
var_id = random.randint(0, len(dataset.features) - 1)
feature_states[state_id][var_id] = "bad_value"
self.assertRaises(
ValueError, model.predict, feature_states
) # Feature_states bad format: value out of domain
feature_states[state_id] = original.copy()
def test_next(self):
print(">> pylfit.semantics.SynchronousConstrained.next(feature_state, targets, rules)")
# Unit test
data = [ \
([0,0,0],[0,0,1]), \
([0,0,0],[1,0,0]), \
([1,0,0],[0,0,0]), \
([0,1,0],[1,0,1]), \
([0,0,1],[0,0,1]), \
([1,1,0],[1,0,0]), \
([1,0,1],[0,1,0]), \
([0,1,1],[1,0,1]), \
([1,1,1],[1,1,0])]
feature_names=["p_t-1","q_t-1","r_t-1"]
target_names=["p_t","q_t","r_t"]
dataset = pylfit.preprocessing.transitions_dataset_from_array(data=data, feature_names=feature_names, target_names=target_names)
model = CDMVLP(features=dataset.features, targets=dataset.targets)
model.compile(algorithm="synchronizer")
model.fit(dataset=dataset)
feature_state = Algorithm.encode_state([0,0,0], model.features)
self.assertEqual(set([tuple(s) for s in SynchronousConstrained.next(feature_state, model.targets, model.rules, model.constraints)]), set([(1,0,0), (0, 0, 1)]))
feature_state = Algorithm.encode_state([1,1,1], model.features)
self.assertEqual(set([tuple(s) for s in SynchronousConstrained.next(feature_state, model.targets, model.rules, model.constraints)]), set([(1,1,0)]))
feature_state = Algorithm.encode_state([0,1,0], model.features)
self.assertEqual(set([tuple(s) for s in SynchronousConstrained.next(feature_state, model.targets, model.rules, model.constraints)]), set([(1,0,1)]))
# Random tests
for i in range(self._nb_tests):
# Apply CDMVLP correctly
model = random_CDMVLP( \
nb_features=random.randint(1,self._nb_features), \
nb_targets=random.randint(1,self._nb_targets), \
max_feature_values=self._nb_feature_values, \
max_target_values=self._nb_target_values, \
algorithm="synchronizer")
feature_state = random.choice(model.feature_states())
feature_state = Algorithm.encode_state(feature_state, model.features)
target_states = SynchronousConstrained.next(feature_state, model.targets, model.rules, model.constraints)
domains = [set() for var in model.targets]
# Apply synchronous semantics
candidates = Synchronous.next(feature_state, model.targets, model.rules)
# Apply constraints
expected = []
for s in candidates:
valid = True
for c in model.constraints:
if c.matches(list(feature_state)+list(s)):
valid = False
#eprint(c, " matches ", feature_state, ", ", s)
break
if valid:
# Decode state with domain values
expected.append(s)
for s2 in target_states:
self.assertTrue(s2 in expected)
for s2 in expected:
self.assertTrue(s2 in target_states)
def test_fit(self):
print(">> CDMVLP.fit(dataset)")
for i in range(self._nb_tests):
dataset = random_StateTransitionsDataset( \
nb_transitions=random.randint(1, self._nb_transitions), \
nb_features=random.randint(1,self._nb_features), \
nb_targets=random.randint(1,self._nb_targets), \
max_feature_values=self._nb_feature_values, \
max_target_values=self._nb_target_values)
for algorithm in self._SUPPORTED_ALGORITHMS:
for verbose in [0, 1]:
model = CDMVLP(features=dataset.features,
targets=dataset.targets)
model.compile(algorithm=algorithm)
f = io.StringIO()
with contextlib.redirect_stderr(f):
model.fit(dataset=dataset, verbose=verbose)
expected_rules, expected_constraints = Synchronizer.fit(
dataset, complete=(algorithm == "synchronizer"))
self.assertEqual(expected_rules, model.rules)
self.assertEqual(expected_constraints, model.constraints)
# Exceptions
#------------
model = CDMVLP(features=dataset.features,
targets=dataset.targets)
model.compile(algorithm=algorithm)
self.assertRaises(ValueError, model.fit, [],
verbose) # dataset is not of valid type
model.algorithm = "bad_value"
self.assertRaises(ValueError, model.fit, dataset,
verbose) # algorithm not supported
model.algorithm = algorithm
original = CDMVLP._COMPATIBLE_DATASETS.copy()
class newdataset(Dataset):
def __init__(self, data, features, targets):
x = ""
CDMVLP._COMPATIBLE_DATASETS = [newdataset]
self.assertRaises(
ValueError, model.fit, newdataset([], [], []),
verbose) # dataset not supported by the algo
CDMVLP._COMPATIBLE_DATASETS = original
model.algorithm = "gula"
original = CDMVLP._ALGORITHMS.copy()
class newdataset(Dataset):
def __init__(self, data, features, targets):
x = ""
CDMVLP._ALGORITHMS = ["gula"]
self.assertRaises(NotImplementedError, model.fit, dataset,
verbose) # dataset not supported yet
CDMVLP._ALGORITHMS = original