def run(labelp="labels.map", datap="datamat.word.pkl"):
labeldic = loadlabels(labelp)
len(labeldic)
x = pickle.load(open(datap))
print len(x["train"][0])
# get entities without labels
allentids = sorted(x["entdic"].items(), key=lambda (a, b): b)[:x["numents"]]
print allentids[:20]
entidsnotinlabeldic = set(map(lambda (a, b): a, allentids)).difference(set(labeldic.keys()))
print len(entidsnotinlabeldic), list(entidsnotinlabeldic)[:20]
wd = {v: k for k, v in x["worddic"].items()}
ed = {v: k for k, v in x["entdic"].items()}
for split in ["train", "test", "valid"]:
print "SPLIT %s" % split
split = x[split]
#print len(split[0])
wocount = 0
for i in range(len(split[0])):
#print ed[split[1][i][0]], ed[split[1][i][0]] in entidsnotinlabeldic
#break
if ed[split[1][i][0]] in entidsnotinlabeldic:
print " ".join(map(lambda x: wd[x] if x in wd else "", list(split[0][i]))), ed[split[1][i][0]]
wocount += 1
else:
print " ".join(map(lambda x: wd[x] if x in wd else "", list(split[0][i]))), labeldic[ed[split[1][i][0]]]
print wocount
def run(labelp="labels.map", datap="datamat.word.pkl"):
labeldic = loadlabels(labelp)
glove = Glove(50)
print "the" in glove
len(labeldic)
x = pickle.load(open(datap))
print len(x["train"][0])
# get entities without labels
allentids = sorted(x["entdic"].items(), key=lambda
(a, b): b)[:x["numents"]]
print allentids[:20]
entidsnotinlabeldic = set(map(lambda (a, b): a,
allentids)).difference(set(labeldic.keys()))
print len(entidsnotinlabeldic), list(entidsnotinlabeldic)[:20]
wd = {v: k for k, v in x["worddic"].items()}
ed = {v: k for k, v in x["entdic"].items()}
alltrainents = set(x["train"][1][:, 0])
allvalidents = set(x["valid"][1][:, 0])
alltestents = set(x["test"][1][:, 0])
print "%d/%d (%.2f%%) test set entities not in training " % (
len(alltestents.difference(alltrainents)), len(alltestents),
len(alltestents.difference(alltrainents)) * 100. / len(alltestents))
print "%d/%d (%.2f%%) validation set entities not in training " % (
len(allvalidents.difference(alltrainents)), len(allvalidents),
len(allvalidents.difference(alltrainents)) * 100. / len(allvalidents))
#print len(allvalidents.difference(alltrainents))
# gather all words in entity labels
labelwords = {}
labelwordsnotinglove = set()
for label in labeldic.values():
for labelw in tokenize(label):
if labelw not in labelwords:
labelwords[labelw] = 0
labelwords[labelw] += 1
if labelw not in glove:
labelwordsnotinglove.add(labelw)
print "%d unique words in labels" % len(labelwords)
print "%d words not in glove" % len(labelwordsnotinglove)
for split in ["train", "test", "valid"]:
#print "SPLIT %s" % split
split = x[split]
#print len(split[0])
wocount = 0
for i in range(len(split[0])):
#print ed[split[1][i][0]], ed[split[1][i][0]] in entidsnotinlabeldic
#break
if ed[split[1][i][0]] in entidsnotinlabeldic:
#print " ".join(map(lambda x: wd[x] if x in wd else "", list(split[0][i]))), ed[split[1][i][0]]
wocount += 1
else:
pass
def run(labelp="labels.map", datap="datamat.word.pkl"):
labeldic = loadlabels(labelp)
glove = Glove(50)
print "the" in glove
len(labeldic)
x = pickle.load(open(datap))
print len(x["train"][0])
# get entities without labels
allentids = sorted(x["entdic"].items(), key=lambda (a, b): b)[:x["numents"]]
print allentids[:20]
entidsnotinlabeldic = set(map(lambda (a, b): a, allentids)).difference(set(labeldic.keys()))
print len(entidsnotinlabeldic), list(entidsnotinlabeldic)[:20]
wd = {v: k for k, v in x["worddic"].items()}
ed = {v: k for k, v in x["entdic"].items()}
alltrainents = set(x["train"][1][:, 0])
allvalidents = set(x["valid"][1][:, 0])
alltestents = set(x["test"][1][:, 0])
print "%d/%d (%.2f%%) test set entities not in training " % (len(alltestents.difference(alltrainents)), len(alltestents), len(alltestents.difference(alltrainents))*100./len(alltestents))
print "%d/%d (%.2f%%) validation set entities not in training " % (
len(allvalidents.difference(alltrainents)), len(allvalidents),
len(allvalidents.difference(alltrainents)) * 100. / len(allvalidents))
#print len(allvalidents.difference(alltrainents))
# gather all words in entity labels
labelwords = {}
labelwordsnotinglove = set()
for label in labeldic.values():
for labelw in tokenize(label):
if labelw not in labelwords:
labelwords[labelw] = 0
labelwords[labelw] += 1
if labelw not in glove:
labelwordsnotinglove.add(labelw)
print "%d unique words in labels" % len(labelwords)
print "%d words not in glove" % len(labelwordsnotinglove)
for split in ["train", "test", "valid"]:
#print "SPLIT %s" % split
split = x[split]
#print len(split[0])
wocount = 0
for i in range(len(split[0])):
#print ed[split[1][i][0]], ed[split[1][i][0]] in entidsnotinlabeldic
#break
if ed[split[1][i][0]] in entidsnotinlabeldic:
#print " ".join(map(lambda x: wd[x] if x in wd else "", list(split[0][i]))), ed[split[1][i][0]]
wocount += 1
else:
pass