def old_rules_vector(sentdict, aux, tree, subtree_positions):
aux_type = aux.type
vector = [bool_to_int(aux_type == 'modal' and modalcheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(aux_type == 'be' and becheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(aux_type == 'have' and havecheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(aux_type == 'do' and docheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(aux_type == 'to' and tocheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(aux_type == 'so' and socheck(sentdict, aux.wordnum, tree, subtree_positions))]
return vector
def test_my_rules(self, original_rules=False, idxs=None):
self.predictions = []
print "Length of test set: %d, length of All_auxs-training vectors: %d" % (
len(self.test_classes),
len(self.all_auxiliaries) - len(self.train_vectors),
)
for i in range(self.pre_oversample_length, len(self.all_auxiliaries)):
if idxs == None or i in idxs:
aux = self.all_auxiliaries.get_aux(i)
sendict = self.sentences.get_sentence(aux.sentnum)
tree = sendict.get_nltk_tree()
word_subtree_positions = nt.get_smallest_subtree_positions(tree)
if not original_rules:
if aux.type == "modal":
self.predictions.append(
vc.bool_to_int(wc.modal_rule(sendict, aux, tree, word_subtree_positions))
)
elif aux.type == "be":
self.predictions.append(vc.bool_to_int(wc.be_rule(sendict, aux)))
elif aux.type == "have":
self.predictions.append(vc.bool_to_int(wc.have_rule(sendict, aux)))
elif aux.type == "do":
self.predictions.append(vc.bool_to_int(wc.do_rule(sendict, aux, tree, word_subtree_positions)))
elif aux.type == "so":
self.predictions.append(vc.bool_to_int(wc.so_rule(sendict, aux)))
elif aux.type == "to":
self.predictions.append(vc.bool_to_int(wc.to_rule(sendict, aux)))
else:
auxidx = aux.wordnum
if aux.type == "modal":
self.predictions.append(
vc.bool_to_int(dv.modalcheck(sendict, auxidx, tree, word_subtree_positions))
)
elif aux.type == "be":
self.predictions.append(
vc.bool_to_int(dv.becheck(sendict, auxidx, tree, word_subtree_positions))
)
elif aux.type == "have":
self.predictions.append(
vc.bool_to_int(dv.havecheck(sendict, auxidx, tree, word_subtree_positions))
)
elif aux.type == "do":
self.predictions.append(
vc.bool_to_int(dv.docheck(sendict, auxidx, tree, word_subtree_positions))
)
elif aux.type == "so":
self.predictions.append(
vc.bool_to_int(dv.socheck(sendict, auxidx, tree, word_subtree_positions))
)
elif aux.type == "to":
self.predictions.append(
vc.bool_to_int(dv.tocheck(sendict, auxidx, tree, word_subtree_positions))
)
def old_rules_vector(sentdict, aux, tree, subtree_positions):
aux_type = aux.type
vector = [
bool_to_int(
aux_type == 'modal'
and modalcheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(
aux_type == 'be'
and becheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(
aux_type == 'have'
and havecheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(
aux_type == 'do'
and docheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(
aux_type == 'to'
and tocheck(sentdict, aux.wordnum, tree, subtree_positions)),
bool_to_int(
aux_type == 'so'
and socheck(sentdict, aux.wordnum, tree, subtree_positions))
]
return vector
def test_my_rules(self, original_rules=False, idxs=None):
self.predictions = []
print 'Length of test set: %d, length of All_auxs-training vectors: %d' % (
len(self.test_classes),
len(self.all_auxiliaries) - len(self.train_vectors))
for i in range(self.pre_oversample_length, len(self.all_auxiliaries)):
if idxs == None or i in idxs:
aux = self.all_auxiliaries.get_aux(i)
sendict = self.sentences.get_sentence(aux.sentnum)
tree = sendict.get_nltk_tree()
word_subtree_positions = nt.get_smallest_subtree_positions(
tree)
if not original_rules:
if aux.type == 'modal':
self.predictions.append(
vc.bool_to_int(
wc.modal_rule(sendict, aux, tree,
word_subtree_positions)))
elif aux.type == 'be':
self.predictions.append(
vc.bool_to_int(wc.be_rule(sendict, aux)))
elif aux.type == 'have':
self.predictions.append(
vc.bool_to_int(wc.have_rule(sendict, aux)))
elif aux.type == 'do':
self.predictions.append(
vc.bool_to_int(
wc.do_rule(sendict, aux, tree,
word_subtree_positions)))
elif aux.type == 'so':
self.predictions.append(
vc.bool_to_int(wc.so_rule(sendict, aux)))
elif aux.type == 'to':
self.predictions.append(
vc.bool_to_int(wc.to_rule(sendict, aux)))
else:
auxidx = aux.wordnum
if aux.type == 'modal':
self.predictions.append(
vc.bool_to_int(
dv.modalcheck(sendict, auxidx, tree,
word_subtree_positions)))
elif aux.type == 'be':
self.predictions.append(
vc.bool_to_int(
dv.becheck(sendict, auxidx, tree,
word_subtree_positions)))
elif aux.type == 'have':
self.predictions.append(
vc.bool_to_int(
dv.havecheck(sendict, auxidx, tree,
word_subtree_positions)))
elif aux.type == 'do':
self.predictions.append(
vc.bool_to_int(
dv.docheck(sendict, auxidx, tree,
word_subtree_positions)))
elif aux.type == 'so':
self.predictions.append(
vc.bool_to_int(
dv.socheck(sendict, auxidx, tree,
word_subtree_positions)))
elif aux.type == 'to':
self.predictions.append(
vc.bool_to_int(
dv.tocheck(sendict, auxidx, tree,
word_subtree_positions)))
