def test_productions(self):
t = Tree.fromstring(
"""
(S
(NP (Det el) (Noun gato))
(VP (Verb come) (NP (Noun pescado) (Adj crudo)))
)
""")
# Bugfix from official test (, start='S')
model = UPCFG([t], start='S')
prods = model.productions()
prods2 = [
ProbabilisticProduction(N('S'), [N('NP'), N('VP')], prob=1.0),
ProbabilisticProduction(N('NP'), [N('Det'), N('Noun')], prob=0.5),
ProbabilisticProduction(N('Det'), ['Det'], prob=1.0),
ProbabilisticProduction(N('Noun'), ['Noun'], prob=1.0),
ProbabilisticProduction(N('VP'), [N('Verb'), N('NP')], prob=1.0),
ProbabilisticProduction(N('Verb'), ['Verb'], prob=1.0),
ProbabilisticProduction(N('NP'), [N('Noun'), N('Adj')], prob=0.5),
ProbabilisticProduction(N('Adj'), ['Adj'], prob=1.0),
]
self.assertEqual(set(prods), set(prods2))
def test_productions(self):
t = Tree.fromstring("""
(S
(NP (Det el) (Noun gato))
(VP (Verb come) (NP (Noun pescado) (Adj crudo)))
)
""")
# Bugfix from official test (, start='S')
model = UPCFG([t], start='S')
prods = model.productions()
prods2 = [
ProbabilisticProduction(N('S'), [N('NP'), N('VP')], prob=1.0),
ProbabilisticProduction(N('NP'), [N('Det'), N('Noun')], prob=0.5),
ProbabilisticProduction(N('Det'), ['Det'], prob=1.0),
ProbabilisticProduction(N('Noun'), ['Noun'], prob=1.0),
ProbabilisticProduction(N('VP'), [N('Verb'), N('NP')], prob=1.0),
ProbabilisticProduction(N('Verb'), ['Verb'], prob=1.0),
ProbabilisticProduction(N('NP'), [N('Noun'), N('Adj')], prob=0.5),
ProbabilisticProduction(N('Adj'), ['Adj'], prob=1.0),
]
self.assertEqual(set(prods), set(prods2))
def test_parse(self):
t = Tree.fromstring("""
(S
(NP (Det el) (Noun gato))
(VP (Verb come) (NP (Noun pescado) (Adj crudo)))
)
""")
model = UPCFG([t], start='S')
sent = 'el gato come pescado crudo'.split()
tags = 'Det Noun Verb Noun Adj'.split()
tagged_sent = list(zip(sent, tags))
tree = model.parse(tagged_sent)
self.assertEqual(tree, t)
def test_parse(self):
t = Tree.fromstring(
"""
(S
(NP (Det el) (Noun gato))
(VP (Verb come) (NP (Noun pescado) (Adj crudo)))
)
""")
model = UPCFG([t], start='S')
sent = 'el gato come pescado crudo'.split()
tags = 'Det Noun Verb Noun Adj'.split()
tagged_sent = list(zip(sent, tags))
tree = model.parse(tagged_sent)
self.assertEqual(tree, t)
def test_parse_no_parse_returns_flat(self):
t = Tree.fromstring("""
(S
(NP (Det el) (Noun gato))
(VP (Verb come) (NP (Noun pescado) (Adj crudo)))
)
""")
model = UPCFG([t], start='S')
sent = 'gato el come pescado crudo'.split()
tags = 'Noun Det Verb Noun Adj'.split()
tagged_sent = list(zip(sent, tags))
tree = model.parse(tagged_sent)
tree2 = Tree.fromstring(
"(S (Noun gato) (Det el) (Verb come) (Noun pescado) (Adj crudo))")
self.assertEqual(tree, tree2)
def test_parse_no_parse_returns_flat(self):
t = Tree.fromstring(
"""
(S
(NP (Det el) (Noun gato))
(VP (Verb come) (NP (Noun pescado) (Adj crudo)))
)
""")
model = UPCFG([t], start='S')
sent = 'gato el come pescado crudo'.split()
tags = 'Noun Det Verb Noun Adj'.split()
tagged_sent = list(zip(sent, tags))
tree = model.parse(tagged_sent)
tree2 = Tree.fromstring("(S (Noun gato) (Det el) (Verb come) (Noun pescado) (Adj crudo))")
self.assertEqual(tree, tree2)
def test_horz_markov_None(self):
t = Tree.fromstring("(NP (Det el) (Noun gato) (Adj negro))")
# Bugfix from official test (, start='NP')
model = UPCFG([t], start='NP') # horzMarkov=None by default
prods = model.productions()
prods2 = [
# the right-binarized productions:
ProbabilisticProduction(N('NP'), [N('Det'), N('NP|<Noun-Adj>')], prob=1.0),
ProbabilisticProduction(N('NP|<Noun-Adj>'), [N('Noun'), N('Adj')], prob=1.0),
ProbabilisticProduction(N('Det'), ['Det'], prob=1.0),
ProbabilisticProduction(N('Noun'), ['Noun'], prob=1.0),
ProbabilisticProduction(N('Adj'), ['Adj'], prob=1.0),
]
self.assertEqual(set(prods), set(prods2))
def test_horz_markov_0(self):
t = Tree.fromstring("(NP (Det el) (Noun gato) (Adj negro))")
model = UPCFG([t], horzMarkov=0)
prods = model.productions()
prods2 = [
# the right-binarized productions:
ProbabilisticProduction(N('NP'), [N('Det'), N('NP|<>')], prob=1.0),
ProbabilisticProduction(N('NP|<>'),
[N('Noun'), N('Adj')],
prob=1.0),
ProbabilisticProduction(N('Det'), ['Det'], prob=1.0),
ProbabilisticProduction(N('Noun'), ['Noun'], prob=1.0),
ProbabilisticProduction(N('Adj'), ['Adj'], prob=1.0),
]
self.assertEqual(set(prods), set(prods2))
def test_init_doesnt_change_trees(self):
t = Tree.fromstring("""
(S
(NP (Det el) (Noun gato))
(VP (Verb come) (NP (Noun pescado) (Adj crudo)))
)
""")
t2 = t.copy(deep=True)
model = UPCFG([t])
self.assertEqual(t, t2)
def test_horz_markov_None(self):
t = Tree.fromstring("(NP (Det el) (Noun gato) (Adj negro))")
# Bugfix from official test (, start='NP')
model = UPCFG([t], start='NP') # horzMarkov=None by default
prods = model.productions()
prods2 = [
# the right-binarized productions:
ProbabilisticProduction(N('NP'),
[N('Det'), N('NP|<Noun-Adj>')],
prob=1.0),
ProbabilisticProduction(N('NP|<Noun-Adj>'),
[N('Noun'), N('Adj')],
prob=1.0),
ProbabilisticProduction(N('Det'), ['Det'], prob=1.0),
ProbabilisticProduction(N('Noun'), ['Noun'], prob=1.0),
ProbabilisticProduction(N('Adj'), ['Adj'], prob=1.0),
]
self.assertEqual(set(prods), set(prods2))
