def test_boundedChunk(self):
s = [["\'"], ["Basta"], ["\'"], [","], ["disse"], ["o"], ["guarda"], ["."], ["\""], ["Agora"], [","], ["só"], ["nos"], ["resta"], ["esperar"], ["\""], [","], ["falou"], ["o"], ["sol."], ["-"], ["A"], ["vida"], ["é"], ["-"], ["afirmou"], ["a"], ["presidente"], ["."]]
bc = baseline.boundedChunk(s)
#for i in range(len(bc)):
# print(s[i][0], bc[i])
self.assertTrue(True)
def createInput(fileName=None, createTest=False):
"""Creates a CSV file with the result of the preprocessing step.
Args:
fileName: The CSV file that will be created
createTest: If the preprocessing will be applyed in the test set
"""
corpus = globoquotes.load(GLOBOQUOTES_FILE)
test = globoquotes.load(GLOBOQUOTES_TEST_FILE)
converter = verbspeech.Converter()
if not fileName:
fileName = INPUT_FILE
open(fileName, 'w').close()
pos = feature.pos(corpus + test, posIndex = 1)
columns = feature.columns(pos)
if createTest:
corpus = test
i = 0
for i in range(len(corpus)):
s = corpus[i]
qs = baseline.quotationStart(s)
qe = baseline.quotationEnd(s, qs)
qb = baseline.quoteBounds(qs, qe)
converter.vsay(s, tokenIndex = 0, posIndex = 1)
#for k in range(len(s)):
# print(k, s[k][0].ljust(30), s[k][1].ljust(10), s[k][7].ljust(5), qs[k], qe[k], qb[k])
# Baseline: X
#print("Create bc...")
bc = baseline.boundedChunk(s)
#print("Create vsn...")
vsn = baseline.verbSpeechNeighb(s)
#print("Create fluc...")
fluc = baseline.firstLetterUpperCase(s)
#print("Identifying quotes...")
quotes = wisinput.interval(qb)
#print("Identifying coreferences...")
coref, labels = wisinput.coref(s, quotes, corefIndex=7)
#print("Creating features...")
feat = feature.create(s, quotes=quotes, coref=coref, posIndex=1, corefIndex=7, quoteBounds=qb, bc=bc, vsn=vsn, fluc=fluc)
#print("Binarying features...")
bfeat = feature.binary(columns, feat)
# Answer: Y
#print("Output: Creating y...")
qbA = [ e[INDEX_QB] for e in s ]
#print("Output: Identifying quotes...")
quotesA = wisinput.interval(qbA)
#print("Output: Quotes = ", len(quotesA))
#print("Output: Identifying coreferences...")
corefA, labelsA = wisinput.corefAnnotated(s, quotes=quotesA, corefIndex=7, gpqIndex=6)
#print("Output: Coref = ", len(corefA))
#print("Output: Creating features...")
featA = feature.create(s, quotes=quotesA, coref=corefA, posIndex=1, corefIndex=7, \
quoteBounds=qbA, bc=bc, vsn=vsn, fluc=fluc, dummy=False)
#print("Output: Binarying features...")
bfeatA = feature.binary(columns, featA)
#print("Output: bFeat = ", len(bfeatA))
with open(fileName, 'a', newline='') as csvfile:
swriter = csv.writer(csvfile, delimiter=';')
for p in range(len(bfeat)):
for q in range(len(bfeat[p])):
swriter.writerow([i, "x"] + list(quotes[p]) + [labels[p][q]] + bfeat[p][q])
for p in range(len(bfeatA)):
for q in range(len(bfeatA[p])):
swriter.writerow([i, "y"] + list(quotesA[p]) + [labelsA[p][q]] + bfeatA[p][q])
def createInput(fileName=None, createTest=False):
"""Creates a CSV file with the result of the preprocessing step.
Args:
fileName: The CSV file that will be created
createTest: If the preprocessing will be applyed in the test set
"""
corpus = bosquequotes.load(BOSQUE_FILE)
test = bosquequotes.load(BOSQUE_TEST_FILE)
converter = verbspeech.Converter()
if not fileName:
fileName = INPUT_FILE
open(fileName, 'w').close()
pos = feature.pos(corpus + test, posIndex=1)
columns = feature.columns(pos)
if createTest:
corpus = test
i = 0
for i in range(len(corpus)):
s = corpus[i]
# qs = baseline.quotationStart(s)
# qe = baseline.quotationEnd(s, qs)
qb = baseline.quoteBounds(s)
converter.vsay(s, tokenIndex=0, posIndex=1)
#for k in range(len(s)):
# print(k, s[k][0].ljust(30), s[k][1].ljust(10), s[k][7].ljust(5), qs[k], qe[k], qb[k])
# Baseline: X
#print("Create bc...")
bc = baseline.boundedChunk(s)
#print("Create vsn...")
vsn = baseline.verbSpeechNeighb(s)
#print("Create fluc...")
fluc = baseline.firstLetterUpperCase(s)
#print("Identifying quotes...")
# quotes = wisinput.interval(qb)
#print("Identifying coreferences...")
# coref, labels = wisinput.coref(s, quotes, corefIndex=7)
quotes, coref, labels = wisinput.candidates(s, depIndex=2)
#print("Creating features...")
feat = feature.create(s,
quotes=quotes,
coref=coref,
posIndex=1,
corefIndex=3,
quoteBounds=qb,
bc=bc,
vsn=vsn,
fluc=fluc)
#print("Binarying features...")
bfeat = feature.binary(columns, feat)
# Answer: Y
#print("Output: Creating y...")
qbA = [e[INDEX_QB] for e in s]
#print("Output: Identifying quotes...")
quotesA = wisinput.interval(qbA)
#print("Output: Quotes = ", len(quotesA))
#print("Output: Identifying coreferences...")
corefA, labelsA = wisinput.corefAnnotated(s,
quotes=quotesA,
depIndex=2,
corefIndex=3,
quoteIndex=4)
#print("Output: Coref = ", len(corefA))
print("Output: Creating features...")
featA = feature.create(s, quotes=quotesA, coref=corefA, posIndex=1, corefIndex=3, \
quoteBounds=qbA, bc=bc, vsn=vsn, fluc=fluc, dummy=False)
#print("Output: Binarying features...")
bfeatA = feature.binary(columns, featA)
#print("Output: bFeat = ", len(bfeatA))
with open(fileName, 'a', newline='') as csvfile:
swriter = csv.writer(csvfile, delimiter=';')
for p in range(len(bfeat)):
for q in range(len(bfeat[p])):
swriter.writerow([i, "x"] + list(quotes[p]) +
[labels[p][q]] + bfeat[p][q])
for p in range(len(bfeatA)):
for q in range(len(bfeatA[p])):
swriter.writerow([i, "y"] + list(quotesA[p]) +
[labelsA[p][q]] + bfeatA[p][q])