def final_scan(exdeflike,
indeflike,
multi=True,
lowercase=False,
externalPOS=False,
outkey="default"):
"""train all of the given training data and then test it on the supplied
test records. make predictions for NE for each token, then print them
out in the format required"""
"""
#load exdef and indef
with open('finalexdef.pickle', 'rb') as infile:
exdeflike = pickle.load(infile)
with open('finalindef.pickle', 'rb') as infile2:
indeflike = pickle.load(infile2)
"""
#load the test data
test_file = '../data/emerging.dev.conll'
with open(test_file, 'r') as f3:
records = re.split("\n[\t]?\n", f3.read().strip())
numrecs = len(records)
os.system("mkdir -p ../data/predictions/" + outkey)
#analyze the test data
# threshold = [0.138, 0.13]
#this is the threshold we found to give the best F1 score
#on the training data, using n-fold validation
tstarts = {
"location": 0.5,
"group": 0.5,
"product": 0.5,
"creative-work": 0.5,
"person": 0.5,
"corporation": 0.5
}
tdiffs = range(-4,
5) # [d for d in range(-49,50)] ## diffs = [-0.49--0.49]
allresults = defaultdict(list)
## begin rounds loop here
for rnd in range(1, 4): ##range(1,2): ##
print "working on rnd " + str(rnd)
print "here are the starting thresholds: "
for NEtype in tstarts:
print NEtype, tstarts[NEtype]
print
results = defaultdict(list)
tdiffs = [tdiff / 10 for tdiff in tdiffs]
# [tdiff/100 for tdiff in tdiffs] [-0.0049--0.0049]
NEthreshs = {
NEtype: [tstarts[NEtype] + tdiff for tdiff in tdiffs]
# [t/1000 for t in range(1001)]
for NEtype in tstarts
}
fs = {}
for NEtype in NEthreshs:
for t in NEthreshs[NEtype]:
fkey = str(t) + "-" + NEtype
threshfile = re.sub("/data/",
"/data/predictions/" + outkey + "/",
test_file + "-" + fkey + ".prediction")
fs[fkey] = [open(threshfile, 'w'), threshfile]
fs[fkey][0].close()
numdone = 0
for record in records:
print str(
100 * numdone / numrecs) + "% done with round " + str(rnd)
numdone += 1
if record: #avoid empty strings
data = [
re.split('\t', d) for d in re.split("\n", record)
if len(re.split("\t", d)) == 2
]
tokens, tags = zip(*data)
uppertokens = list(tokens)
if lowercase:
tokens = [token.lower() for token in tokens]
for NEtype in NEthreshs:
exavedeflike, inavedeflike = NER.test(
tokens,
exdeflike,
indeflike,
multi,
NEtype,
externalPOS=externalPOS,
uppertokens=uppertokens)
for t in NEthreshs[NEtype]:
#keep track of the NE assignments
#for each token with tuples
if lowercase:
assignments = [[token, 'O']
for token in uppertokens if token]
else:
assignments = [[token, 'O'] for token in tokens
if token]
fkey = str(t) + "-" + NEtype
#find the NEs using the _LFD_ function as before
for indices in NER.LFD(tokens, exavedeflike,
inavedeflike, [1.1, t]):
# print t, [tokens[ix] for ix in indices]
# innums = [inavedeflike[ix][1] for ix in indices if ix != indices[0]]
# innums.append(inavedeflike[indices[0]][0])
# print "internal", NER.harmonic_mean(innums)
# raw_input()
n = 0
for index in indices:
if n == 0:
assignments[index][1] = 'B-' + NEtype
else:
assignments[index][1] = 'I-' + NEtype
n += 1 #keep track of position in NE
## write out according to file handles, here
fs[fkey][0] = open(fs[fkey][1], "a")
for i, assignment in enumerate(assignments):
fs[fkey][0].writelines("\t".join(
[assignment[0], tags[i], assignment[1]]) +
"\n")
fs[fkey][0].writelines("\n")
fs[fkey][0].close()
## evaluate all thresholds and all NE types for the best of the round
for fkey in fs:
## fs[fkey][0].close()
NEtype = "-".join(re.split("-", fkey)[1:])
t = float(re.split("-", fkey)[0])
filename = fs[fkey][1]
try:
results[NEtype].append((map(
float,
re.split("\;", [
re.sub("[^0-9\.\;]+", "", re.sub("\d+$|\d\:", "", r))
for r in re.split(
"\n",
subprocess.check_output(
"python2 ../data/wnuteval.py " + filename,
shell=True)) if re.search(NEtype, r)
][0])), t))
except:
results[NEtype].append(([0., 0., 0.], t))
allresults[NEtype].append(tuple(results[NEtype][-1]))
## store the best of this round as tstarts
for NEtype in results:
tstarts[NEtype] = max(results[NEtype], key=lambda x: x[0][2])[1]
print "here are the end-of-round thresholds: "
for NEtype in tstarts:
print NEtype, tstarts[NEtype]
print
with open("../data/predictions/" + outkey + "/allresults.json", "w") as f:
f.writelines(json.dumps([tstarts, allresults]))
return tstarts, allresults
def final_analysis(
exdeflikefile,
indeflikefile,
multi=True,
lowercase=True,
externalPOS=True,
dev=True,
thresholds={
"location": 0.292,
"group": 0.09,
"product": 0.131,
"creative-work": 1.1,
"person": 0.202,
"corporation": 1.1
}):
#load exdef and indef
with open(exdeflikefile) as f:
exdeflike = pickle.load(f)
with open(indeflikefile) as f:
indeflike = pickle.load(f)
"""train all of the given training data and then test it on the supplied
test records. make predictions for NE for each token, then print them
out in the format required"""
"""
#load exdef and indef
with open('finalexdef.pickle', 'rb') as infile:
exdeflike = pickle.load(infile)
with open('finalindef.pickle', 'rb') as infile2:
indeflike = pickle.load(infile2)
"""
#load the test data
if dev:
test_file = '../data/emerging.dev.conll'
outfilename = "../data/finalpredictions/emerging_" + "_".join(
re.split("_", indeflikefile)[1:3]) + ".dev"
else:
test_file = '../data/emerging.test'
outfilename = "../data/finalpredictions/emerging_" + "_".join(
re.split("_", indeflikefile)[1:3]) + ".test"
with open(test_file, 'r') as f3:
records = re.split("\n[\t]?\n", f3.read().strip())
#analyze the test data
#on the training data, using n-fold validation
# f = open(test_file + ".prediction", 'w')
f = open(outfilename, "w")
# thresholds = {
# ## "location": [0.001, 0.157],
# "location": [1.1, 0.157],
# ## "group": [0.008, 0.199],
# "group": [1.1, 0.199],
# "product": [1.1, 0.215],
# "creative-work": [1.1,0.499],
# ## "person": [0.002, 0.167],
# "person": [1.1, 0.167],
# "corporation": [1.1, 0.218]
# }
for record in records:
if record: #avoid empty strings
if dev:
data = [
re.split('\t', d) for d in re.split("\n", record)
if len(re.split("\t", d)) == 2
]
tokens, tags = zip(*data)
else:
tokens = [
re.split('\t', d)[0] for d in re.split("\n", record)
if len(re.split("\t", d)) == 1
]
uppertokens = list(tokens)
if lowercase:
tokens = [token.lower() for token in tokens]
#keep track of the NE assignments for each token with tuples
if lowercase:
assignments = [[token, 'O'] for token in uppertokens if token]
else:
assignments = [[token, 'O'] for token in tokens if token]
##
predictions = {}
for NEtype in thresholds:
exavedeflike, inavedeflike = NER.test(tokens,
exdeflike,
indeflike,
multi,
NEtype,
externalPOS=externalPOS,
uppertokens=uppertokens)
#find the NEs using the _LFD_ function as before
for indices in NER.LFD(tokens, exavedeflike, inavedeflike,
[1.1, thresholds[NEtype]]):
# if exavedeflike[indices] >= thresholds[NEtype][0]:
# print NEtype+": ", [tokens[ix] for ix in indices]
# print "external", exavedeflike[indices]
innums = [
inavedeflike[ix][1] for ix in indices
if ix != indices[0]
]
innums.append(inavedeflike[indices[0]][0])
## print "internal", NER.harmonic_mean(innums)
predictions[(indices, NEtype)] = [
len(list(indices)), exavedeflike[indices],
NER.harmonic_mean(innums)
]
##
for indices, NEtype in predictions:
thissize = predictions[(indices, NEtype)][0]
thislike = predictions[(indices, NEtype)][2]
for otherindices, otherNEtype in predictions:
thatsize = predictions[(otherindices, otherNEtype)][0]
thatlike = predictions[(otherindices, otherNEtype)][2]
broken = True
for ix in otherindices:
if ix in indices:
if otherindices[0] < indices[0]:
print("precidence, avoided: ",
[tokens[ix] for ix in indices], " over ",
[tokens[ix] for ix in otherindices])
break
elif otherindices[0] == indices[0]:
if thatsize > thissize:
print("size, avoided: ",
[tokens[ix]
for ix in indices], " over ",
[tokens[ix] for ix in otherindices])
break
elif thatlike > thislike:
print("likelihood, avoided: " + NEtype,
[tokens[ix] for ix in indices
], " over " + otherNEtype,
[tokens[ix] for ix in otherindices])
break
else:
broken = False
if broken:
break
else:
print NEtype + ": ", [tokens[ix] for ix in indices]
print "internal", thislike
##
#assign 'B' to the first, 'I' to the rest
n = 0
for index in indices:
if n == 0:
assignments[index][1] = 'B-' + NEtype
else:
assignments[index][1] = 'I-' + NEtype
n += 1 #keep track of position in NE
##
for i, assignment in enumerate(assignments):
if dev:
f.writelines(
"\t".join([assignment[0], tags[i], assignment[1]]) +
"\n")
else:
f.writelines("\t".join([assignment[0], assignment[1]]) +
"\n")
f.writelines("\n")
