def words(self): return LazyMap(self._word_tokenize, self.text())
def approxrand(a, b, **kwargs):
"""
Returns an approximate significance level between two lists of
independently generated test values.
Approximate randomization calculates significance by randomly drawing
from a sample of the possible permutations. At the limit of the number
of possible permutations, the significance level is exact. The
approximate significance level is the sample mean number of times the
statistic of the permutated lists varies from the actual statistic of
the unpermuted argument lists.
:return: a tuple containing an approximate significance level, the count
of the number of times the pseudo-statistic varied from the
actual statistic, and the number of shuffles
:rtype: tuple
:param a: a list of test values
:type a: list
:param b: another list of independently generated test values
:type b: list
"""
shuffles = kwargs.get('shuffles', 999)
# there's no point in trying to shuffle beyond all possible permutations
shuffles = \
min(shuffles, reduce(operator.mul, xrange(1, len(a) + len(b) + 1)))
stat = kwargs.get('statistic', lambda lst: float(sum(lst)) / len(lst))
verbose = kwargs.get('verbose', False)
if verbose:
print 'shuffles: %d' % shuffles
actual_stat = fabs(stat(a) - stat(b))
if verbose:
print 'actual statistic: %f' % actual_stat
print '-' * 60
c = 1e-100
lst = LazyConcatenation([a, b])
indices = range(len(a) + len(b))
for i in range(shuffles):
if verbose and i % 10 == 0:
print 'shuffle: %d' % i
shuffle(indices)
pseudo_stat_a = stat(LazyMap(lambda i: lst[i], indices[:len(a)]))
pseudo_stat_b = stat(LazyMap(lambda i: lst[i], indices[len(a):]))
pseudo_stat = fabs(pseudo_stat_a - pseudo_stat_b)
if pseudo_stat >= actual_stat:
c += 1
if verbose and i % 10 == 0:
print 'pseudo-statistic: %f' % pseudo_stat
print 'significance: %f' % (float(c + 1) / (i + 1))
print '-' * 60
significance = float(c + 1) / (shuffles + 1)
if verbose:
print 'significance: %f' % significance
if betai:
for phi in [0.01, 0.05, 0.10, 0.15, 0.25, 0.50]:
print "prob(phi<=%f): %f" % (phi, betai(c, shuffles, phi))
return (significance, c, shuffles)
def sents(self, fileids=None):
self._require(self.WORDS)
return LazyMap(self._get_words, self._grids(fileids))
def _get_words(self, fileid, sent, stem, relation, pos, strip_space,
replace):
xmldoc = ElementTree.parse(fileid).getroot()
# processing each xml doc
results = []
for xmlsent in xmldoc.findall('.//{%s}u' % NS):
sents = [xmlsent.get('uID'), xmlsent.get('who')]
#print(xmlsent.get('uID'), xmlsent.get('who'))
# select speakers
#if speaker == 'ALL' or xmlsent.get('who') in speaker:
for xmlword in xmlsent.findall('.//{%s}w' % NS):
infl = None
suffixStem = None
suffixTag = None
# getting replaced words
if replace and xmlsent.find('.//{%s}w/{%s}replacement' %
(NS, NS)):
xmlword = xmlsent.find('.//{%s}w/{%s}replacement/{%s}w' %
(NS, NS, NS))
elif replace and xmlsent.find('.//{%s}w/{%s}wk' % (NS, NS)):
xmlword = xmlsent.find('.//{%s}w/{%s}wk' % (NS, NS))
# get text
if xmlword.text:
word = xmlword.text
else:
word = ''
# strip tailing space
if strip_space:
word = word.strip()
# stem
'''
if relation or stem:
try:
xmlstem = xmlword.find('.//{%s}stem' % NS)
word = xmlstem.text
except AttributeError as e:
pass
# if there is an inflection
try:
xmlinfl = xmlword.find('.//{%s}mor/{%s}mw/{%s}mk'
% (NS,NS,NS))
word += '-' + xmlinfl.text
except:
pass
# if there is a suffix
try:
xmlsuffix = xmlword.find('.//{%s}mor/{%s}mor-post/{%s}mw/{%s}stem'
% (NS,NS,NS,NS))
suffixStem = xmlsuffix.text
except AttributeError:
suffixStem = ""
if suffixStem:
word += "~"+suffixStem
# pos'''
if relation or pos:
try:
xmlpos = xmlword.findall(".//{%s}c" % NS)
xmlpos2 = xmlword.findall(".//{%s}s" % NS)
if xmlpos2 != []:
tag = xmlpos[0].text + ":" + xmlpos2[0].text
else:
tag = xmlpos[0].text
except (AttributeError, IndexError) as e:
tag = ""
try:
xmlsuffixpos = xmlword.findall(
'.//{%s}mor/{%s}mor-post/{%s}mw/{%s}pos/{%s}c' %
(NS, NS, NS, NS, NS))
xmlsuffixpos2 = xmlword.findall(
'.//{%s}mor/{%s}mor-post/{%s}mw/{%s}pos/{%s}s' %
(NS, NS, NS, NS, NS))
if xmlsuffixpos2:
suffixTag = xmlsuffixpos[
0].text + ":" + xmlsuffixpos2[0].text
else:
suffixTag = xmlsuffixpos[0].text
except:
pass
if suffixTag:
tag += "~" + suffixTag
word = (word, tag)
# relational
# the gold standard is stored in
# <mor></mor><mor type="trn"><gra type="grt">
if relation == True:
for xmlstem_rel in xmlword.findall('.//{%s}mor/{%s}gra' %
(NS, NS)):
if not xmlstem_rel.get('type') == 'grt':
word = (word[0], word[1],
xmlstem_rel.get('index') + "|" +
xmlstem_rel.get('head') + "|" +
xmlstem_rel.get('relation'))
else:
word = (word[0], word[1], word[2], word[0],
word[1], xmlstem_rel.get('index') + "|" +
xmlstem_rel.get('head') + "|" +
xmlstem_rel.get('relation'))
try:
for xmlpost_rel in xmlword.findall(
'.//{%s}mor/{%s}mor-post/{%s}gra' %
(NS, NS, NS)):
if not xmlpost_rel.get('type') == 'grt':
suffixStem = (suffixStem[0], suffixStem[1],
xmlpost_rel.get('index') + "|" +
xmlpost_rel.get('head') + "|" +
xmlpost_rel.get('relation'))
else:
suffixStem = (suffixStem[0], suffixStem[1],
suffixStem[2], suffixStem[0],
suffixStem[1],
xmlpost_rel.get('index') + "|" +
xmlpost_rel.get('head') + "|" +
xmlpost_rel.get('relation'))
except:
pass
sents.append(word)
#print(sents)
if sent or relation:
results.append(sents)
else:
results.extend(sents)
return LazyMap(lambda x: x, results)
def sents(self, fileids=None):
return LazyMap(untagged, self.elements(fileids))
def tagged_words(self, fileids=None, tagset=None):
self._require(self.WORDS, self.POS)
def get_tagged_words(grid):
return self._get_tagged_words(grid, tagset)
return LazyConcatenation(LazyMap(get_tagged_words,
self._grids(fileids)))
def sents(self):
# LazyMap from nltk.util:
return LazyMap(lambda t: t.leaves(), self.get_trees())
print("Running on dev")
test_data = [json.loads(line) for line in open_file("twt.dev.json")]
else:
print("Running on test")
test_data = [json.loads(line) for line in open_file("twt.test.json")]
test_data = handle_lowfreq_words(vocab)(test_data)
twitter_model = hmm.HiddenMarkovModelTagger(symbols=hmm_model.symbols,
states=tagset,
transitions=transition_model,
outputs=emission_model,
priors=init_model)
# Compute the accuracy - we can call this, but then we just do extra decoding
# work. What we really need is just call nltk.metrics.accuracy on the gold and
# predicted.
# twitter_model.test( test_data )
# Compute the confusion matrix, technically we would be doing this twice, as
# when computing accuracy we would've already done this. It would be more
# optimal to modify the hmm library. But meh.
gold = tag_list(test_data)
unlabeled_data = LazyMap(unlabeled_words, test_data)
predicted_labels = list(LazyMap(twitter_model.tag, unlabeled_data))
predicted = tag_list(predicted_labels)
acc = accuracy(gold, predicted)
print("Accuracy: ", acc)
cm = ConfusionMatrix(gold, predicted)
print(cm.pretty_format(sort_by_count=True, show_percents=True,
truncate=25))
def transform(labeled_symbols):
return LazyMap(relabel, labeled_symbols)
def tagged_words(self):
return LazyConcatenation(LazyMap(self._tagger.tag, self.sents()))
def chunked_sents(self):
return LazyMap(self._chunker.chunk, self.tagged_sents())
def tagged_sents(self):
return LazyMap(self._tagger.tag, self.sents())
def zipzip(*lists):
return LazyMap(lambda lst: zip(*lst), LazyZip(*lists))
def parsed_sents(self):
return LazyMap(self._chunker.parse, self.tagged_sents())
def tagged_sents(self, fileids=None):
def f(s):
return [(w, simple_tag(t)) for w, t in s]
return LazyMap(f, super().tagged_sents(fileids))
def tagged_sents(self, fileids=None):
self._require(self.WORDS, self.POS)
return LazyMap(self._get_tagged_words, self._grids(fileids))
def tagged_sents(self):
# LazyMap from nltk.util:
f = lambda t: [(x,x) for x in t.leaves()]
return LazyMap(f, self.get_trees())
os.remove(test_tmp)
sys.exit(0)
if options.trainer:
if options.pos:
reader = MXPostTaggerCorpusReader(eval(options.corpus))
iob_sents = reader.iob_sents()
tagged_sents = reader.tagged_sents()
corpus = LazyMap(lambda (iob_sent, tagged_sent):
[(iw, tt, iob) for ((iw, iob), (tw, tt))
in zip(iob_sent, tagged_sent)],
LazyZip(iob_sents, tagged_sents))
else:
iob_sents = eval(options.corpus).iob_sents()
corpus = LazyMap(lambda iob_sent:
[(w, None, i) for w, i in iob_sent], iob_sents)
num_train, num_test = options.numsents
num_train = num_train or int(len(corpus) * 0.9)
num_test = num_test or (len(corpus) - num_train)
train = corpus[:num_train]
test = corpus[num_train:num_train + num_test]
trainer = eval(options.trainer)
if options.verbose:
print 'Training %s with %d sentences' % \
(options.trainer, num_train)
ner = trainer(train,
feature_detector=NERChunkTaggerFeatureDetector,
chunk_types=_NE_CHUNK_TYPES,
verbose=options.verbose)
def parsed_sents(self, fileids=None, pos_in_tree=None, tagset=None):
self._require(self.WORDS, self.POS, self.TREE)
if pos_in_tree is None: pos_in_tree = self._pos_in_tree
def get_parsed_sent(grid): # capture pos_in_tree as local var
return self._get_parsed_sent(grid, pos_in_tree, tagset)
return LazyMap(get_parsed_sent, self._grids(fileids))
def iterate_from(self, start):
f = lambda d: d.get(self.field, '')
return iter(
LazyMap(f, self.collection.find(fields=[self.field], skip=start)))
def tagged_sents(self):
# LazyMap from nltk.util:
return LazyMap(lambda t: t.pos(), self.get_trees())
def words(self):
return LazyConcatenation(LazyMap(self._word_tokenize, self.text()))
def parsed_sents(self, fileids=None):
return LazyMap(parsed, self.elements(fileids))
def sents(self):
return LazyConcatenation(LazyMap(self._sent_tokenize, self.text()))
def tagged_lem_sents(self, fileids=None, tagset=None):
self._require(self.WORDS, self.POS, self.LEMMA)
def get_tagged_lemmas(grid):
return self._get_tagged_lemmas(grid, tagset)
return LazyMap(get_tagged_lemmas, self._grids(fileids))
def dep_srl_spans(self, fileids=None):
self._require(self.SRL, self.FILLPRED)
return LazyMap(self._get_dep_srl_spans, self._grids(fileids))
def words(self, fileids=None):
self._require(self.WORDS)
return LazyConcatenation(LazyMap(self._get_words,
self._grids(fileids)))
def _get_words(
self, fileid, speaker, sent, stem, relation, pos, strip_space, replace
):
if (
isinstance(speaker, str) and speaker != "ALL"
): # ensure we have a list of speakers
speaker = [speaker]
xmldoc = ElementTree.parse(fileid).getroot()
# processing each xml doc
results = []
for xmlsent in xmldoc.findall(".//{%s}u" % NS):
sents = []
# select speakers
if speaker == "ALL" or xmlsent.get("who") in speaker:
for xmlword in xmlsent.findall(".//{%s}w" % NS):
infl = None
suffixStem = None
suffixTag = None
# getting replaced words
if replace and xmlsent.find(".//{%s}w/{%s}replacement" % (NS, NS)):
xmlword = xmlsent.find(
".//{%s}w/{%s}replacement/{%s}w" % (NS, NS, NS)
)
elif replace and xmlsent.find(".//{%s}w/{%s}wk" % (NS, NS)):
xmlword = xmlsent.find(".//{%s}w/{%s}wk" % (NS, NS))
# get text
if xmlword.text:
word = xmlword.text
else:
word = ""
# strip tailing space
if strip_space:
word = word.strip()
# stem
if relation or stem:
try:
xmlstem = xmlword.find(".//{%s}stem" % NS)
word = xmlstem.text
except AttributeError as e:
pass
# if there is an inflection
try:
xmlinfl = xmlword.find(
".//{%s}mor/{%s}mw/{%s}mk" % (NS, NS, NS)
)
word += "-" + xmlinfl.text
except:
pass
# if there is a suffix
try:
xmlsuffix = xmlword.find(
".//{%s}mor/{%s}mor-post/{%s}mw/{%s}stem"
% (NS, NS, NS, NS)
)
suffixStem = xmlsuffix.text
except AttributeError:
suffixStem = ""
if suffixStem:
word += "~" + suffixStem
# pos
if relation or pos:
try:
xmlpos = xmlword.findall(".//{%s}c" % NS)
xmlpos2 = xmlword.findall(".//{%s}s" % NS)
if xmlpos2 != []:
tag = xmlpos[0].text + ":" + xmlpos2[0].text
else:
tag = xmlpos[0].text
except (AttributeError, IndexError) as e:
tag = ""
try:
xmlsuffixpos = xmlword.findall(
".//{%s}mor/{%s}mor-post/{%s}mw/{%s}pos/{%s}c"
% (NS, NS, NS, NS, NS)
)
xmlsuffixpos2 = xmlword.findall(
".//{%s}mor/{%s}mor-post/{%s}mw/{%s}pos/{%s}s"
% (NS, NS, NS, NS, NS)
)
if xmlsuffixpos2:
suffixTag = (
xmlsuffixpos[0].text + ":" + xmlsuffixpos2[0].text
)
else:
suffixTag = xmlsuffixpos[0].text
except:
pass
if suffixTag:
tag += "~" + suffixTag
word = (word, tag)
# relational
# the gold standard is stored in
# <mor></mor><mor type="trn"><gra type="grt">
if relation == True:
for xmlstem_rel in xmlword.findall(
".//{%s}mor/{%s}gra" % (NS, NS)
):
if not xmlstem_rel.get("type") == "grt":
word = (
word[0],
word[1],
xmlstem_rel.get("index")
+ "|"
+ xmlstem_rel.get("head")
+ "|"
+ xmlstem_rel.get("relation"),
)
else:
word = (
word[0],
word[1],
word[2],
word[0],
word[1],
xmlstem_rel.get("index")
+ "|"
+ xmlstem_rel.get("head")
+ "|"
+ xmlstem_rel.get("relation"),
)
try:
for xmlpost_rel in xmlword.findall(
".//{%s}mor/{%s}mor-post/{%s}gra" % (NS, NS, NS)
):
if not xmlpost_rel.get("type") == "grt":
suffixStem = (
suffixStem[0],
suffixStem[1],
xmlpost_rel.get("index")
+ "|"
+ xmlpost_rel.get("head")
+ "|"
+ xmlpost_rel.get("relation"),
)
else:
suffixStem = (
suffixStem[0],
suffixStem[1],
suffixStem[2],
suffixStem[0],
suffixStem[1],
xmlpost_rel.get("index")
+ "|"
+ xmlpost_rel.get("head")
+ "|"
+ xmlpost_rel.get("relation"),
)
except:
pass
sents.append(word)
if sent or relation:
results.append(sents)
else:
results.extend(sents)
return LazyMap(lambda x: x, results)
def srl_spans(self, fileids=None):
self._require(self.SRL)
return LazyMap(self._get_srl_spans, self._grids(fileids))
def tagged_words(self, fileids=None, simplify_tags=False):
self._require(self.WORDS, self.POS)
def get_tagged_words(grid):
return self._get_tagged_words(grid, simplify_tags)
return LazyConcatenation(LazyMap(get_tagged_words,
self._grids(fileids)))
