def loadtraincorpus(corpusfmt, traincorpus, binarization, punct, functions,
morphology, removeempty, ensureroot, transformations,
relationalrealizational):
"""Load the training corpus."""
train = treebank.READERS[corpusfmt](traincorpus.path,
encoding=traincorpus.encoding, headrules=binarization.headrules,
headfinal=True, headreverse=False, removeempty=removeempty,
ensureroot=ensureroot, punct=punct,
functions=functions, morphology=morphology)
if isinstance(traincorpus.numsents, float):
traincorpus.numsents = int(traincorpus.numsents * len(train.sents()))
traintrees = train.itertrees(None, traincorpus.numsents)
trees, sents = zip(*[treesent for _, treesent in traintrees
if 1 <= len(treesent[1]) <= traincorpus.maxwords])
logging.info('%d training sentences after length restriction <= %d',
len(trees), traincorpus.maxwords)
if not trees:
raise ValueError('training corpus (selection) should be non-empty.')
if transformations:
trees = [treebanktransforms.transform(tree, sent, transformations)
for tree, sent in zip(trees, sents)]
if relationalrealizational:
trees = [treebanktransforms.rrtransform(
tree, **relationalrealizational)[0] for tree in trees]
train_tagged_sents = [[(word, tag) for word, (_, tag)
in zip(sent, sorted(tree.pos()))]
for tree, sent in zip(trees, sents)]
return trees, sents, train_tagged_sents
def test_transforms():
"""Test reversibility of Tiger transformations."""
from discodop.treebanktransforms import transform, reversetransform, \
bracketings
from discodop.treebank import NegraCorpusReader, handlefunctions
headrules = None # 'alpino.headrules'
n = NegraCorpusReader('alpinosample.export', headrules=headrules)
nn = NegraCorpusReader('alpinosample.export', headrules=headrules)
transformations = ('S-RC', 'VP-GF', 'NP')
trees = [
transform(tree, sent, transformations)
for tree, sent in zip(nn.trees().values(),
nn.sents().values())
]
print('\ntransformed')
correct = exact = e = 0
for a, b, c, d in islice(
zip(n.trees().values(),
n.sents().values(), trees, count()), 100):
transformc = reversetransform(c.copy(True), b, transformations)
c1 = bracketings(canonicalize(a))
c2 = bracketings(canonicalize(transformc))
z = -1 # 825
if c1 != c2 or e == z:
precision = len(set(c1) & set(c2)) / len(set(c1))
recall = len(set(c1) & set(c2)) / len(set(c2))
if precision != 1.0 or recall != 1.0 or d == z:
print(
d, ' '.join(':'.join((str(n), a.encode('unicode-escape')))
for n, a in enumerate(b)))
print('no match', precision, recall)
print(len(c1), len(c2), 'gold-transformed',
set(c2) - set(c1), 'transformed-gold',
set(c1) - set(c2))
print(a)
print(transformc)
handlefunctions('add', a)
print(a, '\n', b, '\n\n')
else:
correct += 1
else:
exact += 1
correct += 1
e += 1
print('matches', correct, '/', e, 100 * correct / e, '%')
print('exact', exact)
def test_transforms():
"""Test reversibility of Tiger transformations."""
from discodop.treebanktransforms import transform, reversetransform, \
bracketings
from discodop.treebank import NegraCorpusReader, handlefunctions
headrules = None # 'alpino.headrules'
n = NegraCorpusReader('alpinosample.export', headrules=headrules)
nn = NegraCorpusReader('alpinosample.export', headrules=headrules)
transformations = ('S-RC', 'VP-GF', 'NP')
trees = [transform(tree, sent, transformations)
for tree, sent in zip(nn.trees().values(),
nn.sents().values())]
print('\ntransformed')
correct = exact = d = 0
for a, b, c in islice(zip(n.trees().values(),
trees, n.sents().values()), 100):
transformb = reversetransform(b.copy(True), transformations)
b1 = bracketings(canonicalize(a))
b2 = bracketings(canonicalize(transformb))
z = -1 # 825
if b1 != b2 or d == z:
precision = len(set(b1) & set(b2)) / len(set(b1))
recall = len(set(b1) & set(b2)) / len(set(b2))
if precision != 1.0 or recall != 1.0 or d == z:
print(d, ' '.join(':'.join((str(n),
a.encode('unicode-escape'))) for n, a in enumerate(c)))
print('no match', precision, recall)
print(len(b1), len(b2), 'gold-transformed', set(b2) - set(b1),
'transformed-gold', set(b1) - set(b2))
print(a)
print(transformb)
handlefunctions('add', a)
print(a, '\n', b, '\n\n')
else:
correct += 1
else:
exact += 1
correct += 1
d += 1
print('matches', correct, '/', d, 100 * correct / d, '%')
print('exact', exact)
def test_transform(self):
from discodop.treebanktransforms import transform, reversetransform, \
bracketings
from discodop.treebank import NegraCorpusReader
n = NegraCorpusReader('alpinosample.export')
for transformations in (('FUNC-NODE', ), ('MORPH-NODE', ),
('LEMMA-NODE', ), ('FUNC-NODE', 'MORPH-NODE',
'LEMMA-NODE')):
nn = NegraCorpusReader('alpinosample.export')
trees = [
transform(tree, sent, transformations)
for tree, sent in zip(nn.trees().values(),
nn.sents().values())
]
for a, b in islice(zip(n.trees().values(), trees), 100):
before = bracketings(canonicalize(a))
transformb = reversetransform(b.copy(True), transformations)
after = bracketings(canonicalize(transformb))
assert before == after, (
'mismatch with %r\nbefore: %r\nafter: %r' %
(transformations, before, after))
def test_transform(self):
from discodop.treebanktransforms import transform, reversetransform, \
bracketings
from discodop.treebank import NegraCorpusReader
n = NegraCorpusReader('alpinosample.export')
for transformations in (
('FUNC-NODE', ),
('MORPH-NODE', ),
('LEMMA-NODE', ),
('FUNC-NODE', 'MORPH-NODE', 'LEMMA-NODE')):
nn = NegraCorpusReader('alpinosample.export')
trees = [transform(tree, sent, transformations)
for tree, sent in zip(nn.trees().values(),
nn.sents().values())]
for a, b in islice(zip(n.trees().values(), trees), 100):
before = bracketings(canonicalize(a))
transformb = reversetransform(b.copy(True), transformations)
after = bracketings(canonicalize(transformb))
assert before == after, (
'mismatch with %r\nbefore: %r\nafter: %r' % (
transformations, before, after))
def main():
"""Command line interface for applying tree(bank) transforms."""
import io
from getopt import gnu_getopt, GetoptError
from discodop import treebanktransforms
actions = {'none': None, 'introducepreterminals': introducepreterminals,
'splitdisc': None, 'mergedisc': mergediscnodes, 'transform': None,
'unbinarize': unbinarize, 'binarize': None, 'optimalbinarize': None}
flags = ('markorigin markheads leftunary rightunary tailmarker '
'renumber reverse'.split())
options = ('inputfmt= outputfmt= inputenc= outputenc= slice= ensureroot= '
'punct= headrules= functions= morphology= lemmas= factor= '
'markorigin= maxlen= fmt= enc= transforms=').split()
try:
opts, args = gnu_getopt(sys.argv[1:], 'h:v:', flags + options)
if not 1 <= len(args) <= 3:
raise GetoptError('error: expected 1, 2, or 3 positional arguments')
except GetoptError as err:
print('error: %r\n%s' % (err, USAGE), file=sys.stderr)
sys.exit(2)
opts, action = dict(opts), args[0]
if action not in actions:
print('unrecognized action: %r\navailable actions: %s' % (
action, ', '.join(actions)), file=sys.stderr)
sys.exit(2)
if '--fmt' in opts:
opts['--inputfmt'] = opts['--outputfmt'] = opts['--fmt']
if '--enc' in opts:
opts['--inputenc'] = opts['--outputenc'] = opts['--enc']
if opts.get('--outputfmt', WRITERS[0]) not in WRITERS:
print('unrecognized output format: %r\navailable formats: %s' % (
opts.get('--outputfmt'), ' '.join(WRITERS)), file=sys.stderr)
sys.exit(2)
infilename = args[1] if len(args) >= 2 and args[1] != '-' else '/dev/stdin'
outfilename = args[2] if len(args) == 3 and args[2] != '-' else '/dev/stdout'
# open corpus
corpus = READERS[opts.get('--inputfmt', 'export')](
infilename,
encoding=opts.get('--inputenc', 'utf-8'),
headrules=opts.get('--headrules'), markheads='--markheads' in opts,
ensureroot=opts.get('--ensureroot'), punct=opts.get('--punct'),
functions=opts.get('--functions'),
morphology=opts.get('--morphology'),
lemmas=opts.get('--lemmas'))
start, end = opts.get('--slice', ':').split(':')
start, end = (int(start) if start else None), (int(end) if end else None)
trees = corpus.itertrees(start, end)
if '--maxlen' in opts:
maxlen = int(opts['--maxlen'])
trees = ((key, (tree, sent)) for key, (tree, sent) in trees
if len(sent) <= maxlen)
if '--renumber' in opts:
trees = (('%8d' % n, treesent)
for n, (_, treesent) in enumerate(trees, 1))
# select transformation
transform = actions[action]
if action in ('binarize', 'optimalbinarize'):
h = int(opts.get('-h', 999))
v = int(opts.get('-v', 1))
if action == 'binarize':
factor = opts.get('--factor', 'right')
transform = lambda t, _: binarize(t, factor, h, v,
leftmostunary='--leftunary' in opts,
rightmostunary='--rightunary' in opts,
tailmarker='$' if '--tailmarker' in opts else '')
elif action == 'optimalbinarize':
headdriven = '--headrules' in opts
transform = lambda t, _: optimalbinarize(t, '|', headdriven, h, v)
elif action == 'splitdisc':
transform = lambda t, _: splitdiscnodes(t, '--markorigin' in opts)
elif action == 'unbinarize':
transform = lambda t, _: unbinarize(Tree.convert(t))
elif action == 'transform':
tfs = opts['--transforms'].split(',')
transform = lambda t, s: (treebanktransforms.reversetransform(t, tfs)
if '--reverse' in opts
else treebanktransforms.transform(t, s, tfs))
if transform is not None: # NB: transform cannot affect (no. of) terminals
trees = ((key, (transform(tree, sent), sent)) for key, (tree, sent) in trees)
# read, transform, & write trees
headrules = None
if opts.get('--outputfmt') in ('mst', 'conll'):
if not opts.get('--headrules'):
raise ValueError('need head rules for dependency conversion')
headrules = treebanktransforms.readheadrules(opts.get('--headrules'))
cnt = 0
if opts.get('--outputfmt') == 'dact':
import alpinocorpus
outfile = alpinocorpus.CorpusWriter(outfilename)
if (action == 'none' and opts.get('--inputfmt') in ('alpino', 'dact')
and set(opts) <= {'--slice', '--inputfmt', '--outputfmt',
'--renumber'}):
for n, (key, block) in islice(enumerate(
corpus.blocks().items(), 1), start, end):
outfile.write('%8d' % n if '--renumber' in opts else key, block)
cnt += 1
else:
for key, (tree, sent) in trees:
outfile.write(str(key), writetree(tree, sent, key, 'alpino'))
cnt += 1
else:
encoding = opts.get('outputenc', 'utf-8')
outfile = io.open(outfilename, 'w', encoding=encoding)
# copy trees verbatim when only taking slice or converting encoding
if (action == 'none' and opts.get('--inputfmt') == opts.get(
'--outputfmt') and set(opts) <= {'--slice', '--inputenc',
'--outputenc', '--inputfmt', '--outputfmt'}):
for block in islice(corpus.blocks().values(), start, end):
outfile.write(block)
cnt += 1
else:
for key, (tree, sent) in trees:
outfile.write(writetree(tree, sent, key,
opts.get('--outputfmt', 'export'), headrules))
cnt += 1
print('%sed %d trees with action %r' % ('convert' if action == 'none'
else 'transform', cnt, action), file=sys.stderr)
def parsetepacoc(
stages=(dict(mode='pcfg', split=True, markorigin=True),
dict(mode='plcfrs', prune=True, k=10000, splitprune=True),
dict(mode='plcfrs', prune=True, k=5000, dop=True,
usedoubledop=True, estimator='dop1', objective='mpp',
sample=False, kbest=True)),
trainmaxwords=999, trainnumsents=25005, testmaxwords=999,
bintype='binarize', h=1, v=1, factor='right', tailmarker='',
markhead=False, revmarkov=False, pospa=False,
leftmostunary=True, rightmostunary=True,
fanout_marks_before_bin=False, transformations=None,
usetagger='stanford', resultdir='tepacoc', numproc=1):
""" Parse the tepacoc test set. """
for stage in stages:
for key in stage:
assert key in DEFAULTSTAGE, 'unrecognized option: %r' % key
stages = [DictObj({k: stage.get(k, v) for k, v in DEFAULTSTAGE.items()})
for stage in stages]
os.mkdir(resultdir)
# Log everything, and send it to stderr, in a format with just the message.
formatstr = '%(message)s'
logging.basicConfig(level=logging.DEBUG, format=formatstr)
# log up to INFO to a results log file
fileobj = logging.FileHandler(filename='%s/output.log' % resultdir)
fileobj.setLevel(logging.INFO)
fileobj.setFormatter(logging.Formatter(formatstr))
logging.getLogger('').addHandler(fileobj)
tepacocids, tepacocsents = readtepacoc()
try:
(corpus_sents, corpus_taggedsents,
corpus_trees, corpus_blocks) = pickle.load(
gzip.open('tiger.pickle.gz', 'rb'))
except IOError: # file not found
corpus = getreader('export')('../tiger/corpus',
'tiger_release_aug07.export',
headrules='negra.headrules' if bintype == 'binarize' else None,
headfinal=True, headreverse=False, punct='move',
encoding='iso-8859-1')
corpus_sents = list(corpus.sents().values())
corpus_taggedsents = list(corpus.tagged_sents().values())
corpus_trees = list(corpus.parsed_sents().values())
if transformations:
corpus_trees = [transform(tree, sent, transformations)
for tree, sent in zip(corpus_trees, corpus_sents)]
corpus_blocks = list(corpus.blocks().values())
pickle.dump((corpus_sents, corpus_taggedsents, corpus_trees,
corpus_blocks), gzip.open('tiger.pickle.gz', 'wb'), protocol=-1)
# test sets (one for each category)
testsets = {}
allsents = []
for cat, catsents in tepacocsents.items():
testset = sents, trees, goldsents, blocks = [], [], [], []
for n, sent in catsents:
if sent != corpus_sents[n]:
logging.error(
'mismatch. sent %d:\n%r\n%r\n'
'not in corpus %r\nnot in tepacoc %r',
n + 1, sent, corpus_sents[n],
[a for a, b in zip_longest(sent, corpus_sents[n])
if a and a != b],
[b for a, b in zip_longest(sent, corpus_sents[n])
if b and a != b])
elif len(corpus_sents[n]) <= testmaxwords:
sents.append(corpus_taggedsents[n])
trees.append(corpus_trees[n])
goldsents.append(corpus_taggedsents[n])
blocks.append(corpus_blocks[n])
allsents.extend(sents)
logging.info('category: %s, %d of %d sentences',
cat, len(testset[0]), len(catsents))
testsets[cat] = testset
testsets['baseline'] = zip(*[sent for n, sent in
enumerate(zip(corpus_taggedsents, corpus_trees,
corpus_taggedsents, corpus_blocks))
if len(sent[1]) <= trainmaxwords
and n not in tepacocids][trainnumsents:trainnumsents + 2000])
allsents.extend(testsets['baseline'][0])
if usetagger:
overridetags = ('PTKANT', 'VAIMP')
taglex = defaultdict(set)
for sent in corpus_taggedsents[:trainnumsents]:
for word, tag in sent:
taglex[word].add(tag)
overridetagdict = {tag:
{word for word, tags in taglex.items()
if tags == {tag}} for tag in overridetags}
tagmap = {'$(': '$[', 'PAV': 'PROAV', 'PIDAT': 'PIAT'}
# the sentences in the list allsents are modified in-place so that
# the relevant copy in testsets[cat][0] is updated as well.
externaltagging(usetagger, '', allsents, overridetagdict, tagmap)
# training set
trees, sents, blocks = zip(*[sent for n, sent in
enumerate(zip(corpus_trees, corpus_sents,
corpus_blocks)) if len(sent[1]) <= trainmaxwords
and n not in tepacocids][:trainnumsents])
getgrammars(trees, sents, stages, bintype, h, v, factor, tailmarker,
revmarkov, leftmostunary, rightmostunary, pospa, markhead,
fanout_marks_before_bin, testmaxwords, resultdir,
numproc, None, False, trees[0].label, None)
del corpus_sents, corpus_taggedsents, corpus_trees, corpus_blocks
results = {}
cnt = 0
parser = Parser(stages, tailmarker=tailmarker,
transformations=transformations)
for cat, testset in sorted(testsets.items()):
if cat == 'baseline':
continue
logging.info('category: %s', cat)
begin = time.clock()
results[cat] = doparsing(parser=parser, testset=testset,
resultdir=resultdir, usetags=True, numproc=numproc,
category=cat)
cnt += len(testset[0])
if numproc == 1:
logging.info('time elapsed during parsing: %g',
time.clock() - begin)
#else: # wall clock time here
goldbrackets = multiset()
totresults = [DictObj(name=stage.name) for stage in stages]
for result in totresults:
result.elapsedtime = [None] * cnt
result.parsetrees = [None] * cnt
result.brackets = multiset()
result.exact = result.noparse = 0
goldblocks = []
goldsents = []
for cat, res in results.items():
logging.info('category: %s', cat)
goldbrackets |= res[2]
goldblocks.extend(res[3])
goldsents.extend(res[4])
for result, totresult in zip(res[0], totresults):
totresult.exact += result.exact
totresult.noparse += result.noparse
totresult.brackets |= result.brackets
totresult.elapsedtime.extend(result.elapsedtime)
oldeval(*res)
logging.info('TOTAL')
oldeval(totresults, goldbrackets)
# write TOTAL results file with all tepacoc sentences (not the baseline)
for stage in stages:
open('TOTAL.%s.export' % stage.name, 'w').writelines(
open('%s.%s.export' % (cat, stage.name)).read()
for cat in list(results) + ['gold'])
# do baseline separately because it shouldn't count towards the total score
cat = 'baseline'
logging.info('category: %s', cat)
oldeval(*doparsing(parser=parser, testset=testsets[cat],
resultdir=resultdir, usetags=True, numproc=numproc, category=cat))
def startexp(
stages=(DEFAULTSTAGE, ), # see above
corpusfmt='export', # choices: export, discbracket, bracket
corpusdir='.',
# filenames may include globbing characters '*' and '?'.
traincorpus='alpinosample.export', trainencoding='utf-8',
testcorpus='alpinosample.export', testencoding='utf-8',
testmaxwords=40,
trainmaxwords=40,
trainnumsents=2,
testnumsents=1, # number of sentences to parse
skiptrain=True, # test set starts after training set
# (useful when they are in the same file)
skip=0, # number of sentences to skip from test corpus
punct=None, # choices: None, 'move', 'remove', 'root'
functions=None, # choices None, 'add', 'remove', 'replace'
morphology=None, # choices: None, 'add', 'replace', 'between'
transformations=None, # apply treebank transformations
# postagging: pass None to use tags from treebank.
postagging=None,
relationalrealizational=None, # do not apply RR-transform
headrules=None, # rules for finding heads of constituents
bintype='binarize', # choices: binarize, optimal, optimalhead
factor='right',
revmarkov=True,
v=1,
h=2,
pospa=False, # when v > 1, add parent annotation to POS tags?
markhead=False, # prepend head to siblings
leftmostunary=True, # start binarization with unary node
rightmostunary=True, # end binarization with unary node
tailmarker='', # with headrules, head is last node and can be marked
fanout_marks_before_bin=False,
evalparam='proper.prm', # EVALB-style parameter file
quiet=False, reallyquiet=False, # quiet=no per sentence results
numproc=1, # increase to use multiple CPUs; None: use all CPUs.
resultdir='results',
rerun=False):
""" Execute an experiment. """
assert bintype in ('optimal', 'optimalhead', 'binarize')
if postagging is not None:
assert set(postagging).issubset({'method', 'model',
'unknownthreshold', 'openclassthreshold', 'simplelexsmooth'})
if postagging['method'] == 'unknownword':
assert postagging['model'] in ('4', '6', 'base')
assert postagging['unknownthreshold'] >= 1
assert postagging['openclassthreshold'] >= 0
else:
assert postagging['method'] in ('treetagger', 'stanford')
if rerun:
assert os.path.exists(resultdir), (
'Directory %r does not exist.'
'--rerun requires a directory '
'with the grammar(s) of a previous experiment.'
% resultdir)
else:
assert not os.path.exists(resultdir), (
'Directory %r exists.\n'
'Use --rerun to parse with existing grammar '
'and overwrite previous results.' % resultdir)
os.mkdir(resultdir)
# Log everything, and send it to stderr, in a format with just the message.
formatstr = '%(message)s'
if reallyquiet:
logging.basicConfig(level=logging.WARNING, format=formatstr)
elif quiet:
logging.basicConfig(level=logging.INFO, format=formatstr)
else:
logging.basicConfig(level=logging.DEBUG, format=formatstr)
# also log to a file
fileobj = logging.FileHandler(filename='%s/output.log' % resultdir)
#fileobj.setLevel(logging.INFO)
fileobj.setLevel(logging.DEBUG)
fileobj.setFormatter(logging.Formatter(formatstr))
logging.getLogger('').addHandler(fileobj)
corpusreader = getreader(corpusfmt)
if not rerun:
corpus = corpusreader(corpusdir, traincorpus, encoding=trainencoding,
headrules=headrules, headfinal=True, headreverse=False,
punct=punct, functions=functions, morphology=morphology)
logging.info('%d sentences in training corpus %s/%s',
len(corpus.parsed_sents()), corpusdir, traincorpus)
if isinstance(trainnumsents, float):
trainnumsents = int(trainnumsents * len(corpus.sents()))
trees = list(corpus.parsed_sents().values())[:trainnumsents]
sents = list(corpus.sents().values())[:trainnumsents]
if transformations:
trees = [transform(tree, sent, transformations)
for tree, sent in zip(trees, sents)]
if relationalrealizational:
trees = [rrtransform(tree, **relationalrealizational)[0]
for tree in trees]
train_tagged_sents = [[(word, tag) for word, (_, tag)
in zip(sent, sorted(tree.pos()))]
for tree, sent in zip(trees, sents)]
blocks = list(corpus.blocks().values())[:trainnumsents]
assert trees, 'training corpus should be non-empty'
logging.info('%d training sentences before length restriction',
len(trees))
trees, sents, blocks = zip(*[sent for sent in zip(trees, sents, blocks)
if len(sent[1]) <= trainmaxwords])
logging.info('%d training sentences after length restriction <= %d',
len(trees), trainmaxwords)
testset = corpusreader(corpusdir, testcorpus, encoding=testencoding,
punct=punct, morphology=morphology, functions=functions)
gold_sents = testset.tagged_sents()
test_parsed_sents = testset.parsed_sents()
if skiptrain:
skip += trainnumsents
logging.info('%d sentences in test corpus %s/%s',
len(testset.parsed_sents()), corpusdir, testcorpus)
logging.info('%d test sentences before length restriction',
len(list(gold_sents)[skip:skip + testnumsents]))
lexmodel = None
test_tagged_sents = gold_sents
if postagging and postagging['method'] in ('treetagger', 'stanford'):
if postagging['method'] == 'treetagger':
# these two tags are never given by tree-tagger,
# so collect words whose tag needs to be overriden
overridetags = ('PTKANT', 'PIDAT')
elif postagging['method'] == 'stanford':
overridetags = ('PTKANT', )
taglex = defaultdict(set)
for sent in train_tagged_sents:
for word, tag in sent:
taglex[word].add(tag)
overridetagdict = {tag:
{word for word, tags in taglex.items() if tags == {tag}}
for tag in overridetags}
tagmap = {'$(': '$[', 'PAV': 'PROAV'}
sents_to_tag = OrderedDict((a, b) for a, b
in islice(gold_sents.items(), skip, skip + testnumsents)
if len(b) <= testmaxwords),
test_tagged_sents = externaltagging(postagging['method'],
postagging['model'], sents_to_tag, overridetagdict, tagmap)
# give these tags to parser
usetags = True
elif postagging and postagging['method'] == 'unknownword' and not rerun:
postagging['unknownwordfun'] = getunknownwordfun(postagging['model'])
# get smoothed probalities for lexical productions
lexresults, msg = getunknownwordmodel(
train_tagged_sents, postagging['unknownwordfun'],
postagging['unknownthreshold'],
postagging['openclassthreshold'])
logging.info(msg)
simplelexsmooth = postagging['simplelexsmooth']
if simplelexsmooth:
lexmodel = lexresults[2:8]
else:
lexmodel, msg = getlexmodel(*lexresults)
logging.info(msg)
# NB: knownwords are all words in training set, lexicon is the subset
# of words that are above the frequency threshold.
# for training purposes we work with the subset, at test time we exploit
# the full set of known words from the training set.
sigs, knownwords, lexicon = lexresults[:3]
postagging['sigs'], postagging['lexicon'] = sigs, knownwords
# replace rare train words with signatures
sents = replaceraretrainwords(train_tagged_sents,
postagging['unknownwordfun'], lexicon)
# make sure gold POS tags are not given to parser
usetags = False
elif postagging and postagging['method'] == 'unknownword' and rerun:
usetags = False
else:
simplelexsmooth = False
# give gold POS tags to parser
usetags = True
# 0: test sentences as they should be handed to the parser,
# 1: gold trees for evaluation purposes
# 2: gold sentence because test sentences may be mangled by unknown word
# model
# 3: blocks from treebank file to reproduce the relevant part of the
# original treebank verbatim.
testset = OrderedDict((a, (test_tagged_sents[a], test_parsed_sents[a],
gold_sents[a], block)) for a, block
in islice(testset.blocks().items(), skip, skip + testnumsents)
if len(test_tagged_sents[a]) <= testmaxwords)
assert test_tagged_sents, 'test corpus should be non-empty'
logging.info('%d test sentences after length restriction <= %d',
len(testset), testmaxwords)
if rerun:
trees = []
sents = []
toplabels = {tree.label for tree in trees} | {
test_parsed_sents[n].label for n in testset}
assert len(toplabels) == 1, 'expected unique ROOT label: %r' % toplabels
top = toplabels.pop()
if rerun:
readgrammars(resultdir, stages, postagging, top)
else:
logging.info('read training & test corpus')
getgrammars(trees, sents, stages, bintype, h, v, factor, tailmarker,
revmarkov, leftmostunary, rightmostunary, pospa, markhead,
fanout_marks_before_bin, testmaxwords, resultdir, numproc,
lexmodel, simplelexsmooth, top, relationalrealizational)
evalparam = evalmod.readparam(evalparam)
evalparam['DEBUG'] = -1
evalparam['CUTOFF_LEN'] = 40
deletelabel = evalparam.get('DELETE_LABEL', ())
deleteword = evalparam.get('DELETE_WORD', ())
begin = time.clock()
parser = Parser(stages, transformations=transformations,
tailmarker=tailmarker, postagging=postagging if postagging
and postagging['method'] == 'unknownword' else None,
relationalrealizational=relationalrealizational)
results = doparsing(parser=parser, testset=testset, resultdir=resultdir,
usetags=usetags, numproc=numproc, deletelabel=deletelabel,
deleteword=deleteword, corpusfmt=corpusfmt, morphology=morphology)
if numproc == 1:
logging.info('time elapsed during parsing: %gs', time.clock() - begin)
for result in results[0]:
nsent = len(result.parsetrees)
header = (' ' + result.name.upper() + ' ').center(35, '=')
evalsummary = evalmod.doeval(OrderedDict((a, b.copy(True))
for a, b in test_parsed_sents.items()), gold_sents,
result.parsetrees, test_tagged_sents if usetags else gold_sents,
evalparam)
coverage = 'coverage: %s = %6.2f' % (
('%d / %d' % (nsent - result.noparse, nsent)).rjust(
25 if any(len(a) > evalparam['CUTOFF_LEN']
for a in gold_sents.values()) else 14),
100.0 * (nsent - result.noparse) / nsent)
logging.info('\n'.join(('', header, evalsummary, coverage)))
return top
