def test_single2(self):
sp = unicode(os.environ["SENNAPATH"])
sp = u"/Users/tuxedocat/Research/tools/senna/"
parser = SennaParser(sp)
txt = open("/Users/tuxedocat/Documents/workspace/Nyanco/sandbox/recognize100.txt").read().split("\n")
txt = [s for s in txt if not s == ""]
# testdata = open("/Users/tuxedocat/Documents/workspace/precure/src/test/sennatags.txt").read()
testdata = []
for s in txt:
testdata.append(parser.parseSentence(s))
for t in testdata:
fe = SentenceFeatures(t, "recognize")
fe.ngrams(n=5)
fe.chunk()
# fe.dependency()
# fe.ne()
# fe.bcv()
# fe.srl()
logging.debug(pformat((fe.SUF, fe.CHK, fe.NER)))
logging.debug(" ".join(fe.SUF))
logging.debug(fe.v_idx)
logging.debug(pformat(fe.features))
vec = DictVectorizer(sparse=True)
array_f = vec.fit_transform(fe.features).toarray()
# logging.debug(pformat(array_f))
raise Exception
def test_with_offset(self):
self.testdata = [doc.split("\n") for doc in open(self.testpath_off).read().split("\n\n") if doc]
fv = []
# print pformat(self.testdata)
for t in self.testdata:
fe = SentenceFeatures(t)
fe.length()
fe.bow()
logging.debug(pformat(zip(fe.SUF, fe.POS)))
logging.debug(pformat(fe.OFFSET))
logging.debug(pformat(fe.features))
fv.append(fe.features)
vec = DictVectorizer(sparse=True)
array_f = vec.fit_transform(fv).toarray()
logging.debug(pformat(array_f))
raise Exception
def test_with_offset(self):
self.testdata = [
doc.split("\n")
for doc in open(self.testpath_off).read().split("\n\n") if doc
]
fv = []
# print pformat(self.testdata)
for t in self.testdata:
fe = SentenceFeatures(t)
fe.length()
fe.bow()
logging.debug(pformat(zip(fe.SUF, fe.POS)))
logging.debug(pformat(fe.OFFSET))
logging.debug(pformat(fe.features))
fv.append(fe.features)
vec = DictVectorizer(sparse=True)
array_f = vec.fit_transform(fv).toarray()
logging.debug(pformat(array_f))
raise Exception
def _get_features_tgt(self, v_corpus=None, cls2id=None, domain="tgt"):
_flist = []
_labellist_int = []
_labellist_str = []
for sid, sdic in enumerate(v_corpus):
v = sdic["label_corr"]
_labelid = cls2id[v]
try:
fe = SentenceFeatures(sdic["parsed_corr"], verb=v, v_idx=sdic["vidx_corr"])
if "chunk" in self.featuretypes:
fe.chunk()
if "3gram" in self.featuretypes:
fe.ngrams(n=3)
if "5gram" in self.featuretypes:
fe.ngrams(n=5)
if "7gram" in self.featuretypes:
fe.ngrams(n=7)
if "dep" in self.featuretypes:
fe.dependency()
if "srl" in self.featuretypes:
fe.srl()
if "ne" in self.featuretypes:
fe.ne()
if "errorprob" in self.featuretypes:
pass
if "topic" in self.featuretypes:
pass
augf = proc_easyadapt(fe.features, domain=domain)
assert augf and _labelid and v
_flist.append(augf)
_labellist_int.append(_labelid)
_labellist_str.append(v)
except ValueError:
logging.debug(pformat("CaseMaker feature extraction: couldn't find the verb"))
except:
print v
# else:
# _flist.append(self.nullfeature)
# _labellist_int.append(_labelid)
# _labellist_str.append(v)
return _flist, _labellist_str, _labellist_int
def get_features(tags=[], v="", v_idx=None, features=[]):
fe = SentenceFeatures(tags=tags, verb=v, v_idx=v_idx)
if "chunk" in features:
fe.chunk()
if "3gram" in features:
fe.ngrams(n=3)
if "5gram" in features:
fe.ngrams(n=5)
if "7gram" in features:
fe.ngrams(n=7)
if "dependency" in features:
fe.dependency()
if "ne" in features:
fe.ne()
if "srl" in features:
fe.srl()
if "topic" in features:
fe.topic()
if "errorprob" in features:
fe.ep()
# print pformat(fe.features)
return proc_easyadapt(fe.features, domain="tgt")
