def has_relation(self, hypo, hyper):
hypo = unicode(hypo)
hyper = unicode(hyper)
hypo_variants = set([hypo, hypo.lower(), lemmatize(hypo), lemmatize(hypo).lower()])
hyper_variants = set([hyper, hyper.lower(), lemmatize(hyper), lemmatize(hyper).lower()])
freqs = [0]
for w in hypo_variants:
for iw in hyper_variants:
if w in self._hypo2hyper and iw in self._hypo2hyper[w]:
freqs.append(self._hypo2hyper[w][iw])
return max(freqs)
def _str_in_str(self, substr, supstr):
substr = unicode(substr).lower()
supstr = unicode(supstr).lower()
if len(substr) < 5: return 0
index = supstr.find(substr)
if index == -1:
substr_l = lemmatize(substr)
index = supstr.find(substr_l)
if index == -1:
supstr_l = lemmatize(supstr)
index = supstr_l.find(substr_l)
if index == -1:
index = supstr_l.find(substr)
if index == -1:
return 0, index
return float(len(substr)) / float(len(supstr)), index
def _str_in_str(self, substr, supstr):
substr = unicode(substr).lower()
supstr = unicode(supstr).lower()
if len(substr) < 5: return 0
index = supstr.find(substr)
if index == -1:
substr_l = lemmatize(substr)
index = supstr.find(substr_l)
if index == -1:
supstr_l = lemmatize(supstr)
index = supstr_l.find(substr_l)
if index == -1:
index = supstr_l.find(substr)
if index == -1:
return 0, index
return float(len(substr)) / float(len(supstr)), index
def has_relation(self, hypo, hyper):
hypo = unicode(hypo)
hyper = unicode(hyper)
hypo_variants = set(
[hypo,
hypo.lower(),
lemmatize(hypo),
lemmatize(hypo).lower()])
hyper_variants = set(
[hyper,
hyper.lower(),
lemmatize(hyper),
lemmatize(hyper).lower()])
freqs = [0]
for w in hypo_variants:
for iw in hyper_variants:
if w in self._hypo2hyper and iw in self._hypo2hyper[w]:
freqs.append(self._hypo2hyper[w][iw])
return max(freqs)
def _is_identical(self, str1, str2):
str1 = re_dash.sub(u" ", str1)
str2 = re_dash.sub(u" ", str2)
return lemmatize(str1) == lemmatize(str2)
def _is_identical(self, str1, str2):
str1 = re_dash.sub(u" ", str1)
str2 = re_dash.sub(u" ", str2)
return lemmatize(str1) == lemmatize(str2)
def __init__(self,
isas_fpath,
min_freq=1,
preprocess=True,
sep='\t',
strip_pos=True,
use_pickle=True,
lowercase=True):
""" Provides access to a ISAs relations from a CSV file "hyponym<TAB>hypernym<TAB>freq" """
if not exists(isas_fpath):
self._hypo2hyper = {}
return
isas_pkl_fpath = isas_fpath + ".pkl"
if use_pickle and exists(isas_pkl_fpath):
pkl = pickle.load(open(isas_pkl_fpath, "rb"))
if "hypo2hyper" in pkl:
hypo2hyper = pkl["hypo2hyper"]
else:
print "Error: cannot find hypo2hyper in ", isas_pkl_fpath
hypo2hyper = {}
if "hyper2hypo" in pkl:
hyper2hypo = pkl["hyper2hypo"]
else:
print "Error: cannot find hyper2hypo in ", isas_pkl_fpath
hyper2hypo = {}
else:
if preprocess:
isas_cln_fpath = isas_fpath + ".cleaned"
preprocess_pandas_csv(isas_fpath, isas_cln_fpath)
isas_df = read_csv(isas_cln_fpath,
sep,
encoding='utf8',
error_bad_lines=False)
try_remove(isas_cln_fpath)
else:
isas_df = read_csv(isas_fpath,
sep,
encoding='utf8',
error_bad_lines=False)
isas_df = isas_df.drop(isas_df[isas_df["freq"] < min_freq].index)
hypo2hyper = defaultdict(dict)
hyper2hypo = defaultdict(dict)
for i, row in isas_df.iterrows():
try:
hypo = unicode(row["hyponym"]).split("#")[0].lower(
) if lowercase else unicode(row["hyponym"]).split("#")[0]
hyper = unicode(row["hypernym"]).split("#")[0].lower(
) if lowercase else unicode(row["hypernym"]).split("#")[0]
freq = int(row["freq"])
hypo_lemma = lemmatize(hypo).lower()
hyper_lemma = lemmatize(hyper).lower()
if hypo not in hypo2hyper or hyper not in hypo2hyper[hypo]:
hypo2hyper[hypo][hyper] = freq
else:
hypo2hyper[hypo][hyper] += freq
if hypo_lemma not in hypo2hyper or hyper_lemma not in hypo2hyper[
hypo_lemma]:
hypo2hyper[hypo_lemma][hyper_lemma] = freq
else:
hypo2hyper[hypo_lemma][hyper_lemma] += freq
if hyper not in hyper2hypo or hypo not in hyper2hypo[hyper]:
hyper2hypo[hyper][hypo] = freq
else:
hyper2hypo[hyper][hypo] += freq
if hyper_lemma not in hyper2hypo or hypo_lemma not in hyper2hypo[
hyper_lemma]:
hyper2hypo[hyper_lemma][hypo_lemma] = freq
else:
hyper2hypo[hyper_lemma][hypo_lemma] += freq
except:
print "Bad row:", row
print format_exc()
print "dictionary is loaded:", len(hypo2hyper)
if use_pickle:
pkl = {"hypo2hyper": hypo2hyper, "hyper2hypo": hyper2hypo}
pickle.dump(pkl, open(isas_pkl_fpath, "wb"))
print "Pickled voc:", isas_pkl_fpath
print "Loaded %d words from: %s" % (len(hypo2hyper), isas_pkl_fpath
if isas_pkl_fpath else isas_fpath)
self._hypo2hyper = hypo2hyper
self._hyper2hypo = hyper2hypo
def __init__(
self, isas_fpath, min_freq=1, preprocess=True, sep="\t", strip_pos=True, use_pickle=True, lowercase=True
):
""" Provides access to a ISAs relations from a CSV file "hyponym<TAB>hypernym<TAB>freq" """
if not exists(isas_fpath):
self._hypo2hyper = {}
return
isas_pkl_fpath = isas_fpath + ".pkl"
if use_pickle and exists(isas_pkl_fpath):
pkl = pickle.load(open(isas_pkl_fpath, "rb"))
if "hypo2hyper" in pkl:
hypo2hyper = pkl["hypo2hyper"]
else:
print "Error: cannot find hypo2hyper in ", isas_pkl_fpath
hypo2hyper = {}
if "hyper2hypo" in pkl:
hyper2hypo = pkl["hyper2hypo"]
else:
print "Error: cannot find hyper2hypo in ", isas_pkl_fpath
hyper2hypo = {}
else:
if preprocess:
isas_cln_fpath = isas_fpath + ".cleaned"
preprocess_pandas_csv(isas_fpath, isas_cln_fpath)
isas_df = read_csv(isas_cln_fpath, sep, encoding="utf8", error_bad_lines=False)
try_remove(isas_cln_fpath)
else:
isas_df = read_csv(isas_fpath, sep, encoding="utf8", error_bad_lines=False)
isas_df = isas_df.drop(isas_df[isas_df["freq"] < min_freq].index)
hypo2hyper = defaultdict(dict)
hyper2hypo = defaultdict(dict)
for i, row in isas_df.iterrows():
try:
hypo = (
unicode(row["hyponym"]).split("#")[0].lower()
if lowercase
else unicode(row["hyponym"]).split("#")[0]
)
hyper = (
unicode(row["hypernym"]).split("#")[0].lower()
if lowercase
else unicode(row["hypernym"]).split("#")[0]
)
freq = int(row["freq"])
hypo_lemma = lemmatize(hypo).lower()
hyper_lemma = lemmatize(hyper).lower()
if hypo not in hypo2hyper or hyper not in hypo2hyper[hypo]:
hypo2hyper[hypo][hyper] = freq
else:
hypo2hyper[hypo][hyper] += freq
if hypo_lemma not in hypo2hyper or hyper_lemma not in hypo2hyper[hypo_lemma]:
hypo2hyper[hypo_lemma][hyper_lemma] = freq
else:
hypo2hyper[hypo_lemma][hyper_lemma] += freq
if hyper not in hyper2hypo or hypo not in hyper2hypo[hyper]:
hyper2hypo[hyper][hypo] = freq
else:
hyper2hypo[hyper][hypo] += freq
if hyper_lemma not in hyper2hypo or hypo_lemma not in hyper2hypo[hyper_lemma]:
hyper2hypo[hyper_lemma][hypo_lemma] = freq
else:
hyper2hypo[hyper_lemma][hypo_lemma] += freq
except:
print "Bad row:", row
print format_exc()
print "dictionary is loaded:", len(hypo2hyper)
if use_pickle:
pkl = {"hypo2hyper": hypo2hyper, "hyper2hypo": hyper2hypo}
pickle.dump(pkl, open(isas_pkl_fpath, "wb"))
print "Pickled voc:", isas_pkl_fpath
print "Loaded %d words from: %s" % (len(hypo2hyper), isas_pkl_fpath if isas_pkl_fpath else isas_fpath)
self._hypo2hyper = hypo2hyper
self._hyper2hypo = hyper2hypo
