def run(self):
self.gold = collections.deque(
tsvlib.iter_tsv_sentences(self.args.gold_file))
self.pred = collections.deque(
tsvlib.iter_tsv_sentences(self.args.prediction_file))
mc_args = dict(debug=self.args.debug,
tractable=not self.args.combinatorial)
c_mwebased = MatchCounter("MWE-based",
**mc_args) # used below for exact match
c_tokbased = MatchCounter("Token-based",
**mc_args) # used below for fuzzy match
while self.gold or self.pred:
self.check_eof()
g = self.gold.popleft()
p = self.pred.popleft()
self.filter_categories(g)
self.filter_categories(p)
self.compare_sentences(g, p)
g_mwes, p_mwes = self.to_mwes(g), self.to_mwes(p)
if self.args.debug:
self.print_debug_pairing(g, p)
self.print_debug_mwes("gold", g_mwes)
self.print_debug_mwes("pred", p_mwes)
c_mwebased.increment_mwebased(g_mwes, p_mwes)
c_tokbased.increment_tokbased(g_mwes, p_mwes)
if self.args.debug:
print("DEBUG:", file=sys.stderr)
self.print_stats(c_mwebased)
self.print_stats(c_tokbased)
def convert(filename, targetfilename, rtl, lang_set_file):
doc = folia.Document(id=os.path.basename(filename.replace('.tsv','')))
if rtl:
doc.metadata['direction'] = 'rtl'
doc.metadata['status'] = 'untouched'
doc.declare(folia.Entity, lang_set_file) #ENTITY-SET definition
doc.declare(folia.AnnotationType.POS, set=POS_SET_URL) #POS-SET definition
text = doc.append(folia.Text)
with open(filename,'r',encoding='utf-8') as f:
for tsv_sentence in tsvlib.iter_tsv_sentences(f):
folia_sentence = folia.Sentence(doc, generate_id_in=text)
text.append(folia_sentence)
for tsv_word in tsv_sentence:
folia_word = folia.Word(doc, text=tsv_word.surface, space=(not tsv_word.nsp), generate_id_in=folia_sentence)
folia_sentence.append(folia_word)
if tsv_word.pos:
folia_word.append(folia.PosAnnotation(doc, cls=tsv_word.pos, annotator="auto", annotatortype=folia.AnnotatorType.AUTO))
mwe_infos = tsv_sentence.mwe_infos()
if mwe_infos:
folia_mwe_list = folia.EntitiesLayer(doc)
folia_sentence.append(folia_mwe_list)
for mweid, mweinfo in mwe_infos.items():
assert mweinfo.category, "Conversion to FoLiA requires all MWEs to have a category" # checkme
folia_words = [folia_sentence[i] for i in mweinfo.word_indexes]
folia_mwe_list.append(folia.Entity, *folia_words, cls=mweinfo.category, annotatortype=folia.AnnotatorType.MANUAL)
doc.save(targetfilename)
def __init__(self, train_file):
self.train_file = train_file
if self.train_file is not None:
self.mwe_fieldindex_sets, self.mwe_field_sets, self.mwe_spans = {}, {}, {}
sents = list(tsvlib.iter_tsv_sentences(self.train_file))
for field_name in TRAIN_FIELD_NAMES:
self.mwe_fieldindex_sets[
field_name] = self._calc_mwe_fieldindex_sets(
field_name, sents)
self.mwe_field_sets[field_name] = self._calc_mwe_field_sets(
field_name)
self.mwe_spans[field_name] = frozenset(
span for sent in sents
for span in sent.iter_mwe_fields_including_span(
field_name))
def run(self):
sys.excepthook = tsvlib.excepthook
for sentence in tsvlib.iter_tsv_sentences(self.args.tsv_file):
for i, token in enumerate(sentence.words, 1):
for mwe_code in token.mwe_codes:
try:
tsvlib.mwe_code_to_id_categ(mwe_code)
except ValueError:
self.warn('MWE codes must look like "3:LVC" or "3"\n' \
'The MWE code {bad!r} is not well-formed',
bad=mwe_code, warntype="ERROR")
if str(i) != token.token_id:
self.warn('Token has rank "{rank}", expected rank "{exp}"',
rank=token.token_id,
exp=i,
warntype="ERROR")
if not self.warned:
print('INFO: The file format looks fine!', file=sys.stderr)
def tsv_(self):
with open(self.corpus, "r+") as f:
tsv_ = list(tsvlib.iter_tsv_sentences(f))
return tsv_
def run(self):
if self.args.debug:
print("DEBUG: LEGEND: {} {} {} {}".format(
GOLDPRED_FMT[(False, False)].format('normal-text'),
GOLDPRED_FMT[(True, False)].format('gold-only'),
GOLDPRED_FMT[(False, True)].format('pred-only'),
GOLDPRED_FMT[(True, True)].format('gold-pred-matched')))
print("DEBUG:")
mc_args = dict(debug=self.args.debug,
tractable=not self.args.combinatorial)
self.gold = collections.deque(
tsvlib.iter_tsv_sentences(self.args.gold_file))
self.pred = collections.deque(
tsvlib.iter_tsv_sentences(self.args.prediction_file))
seen = SeenInfo(self.args.train_file)
base_stats = Statistics(mc_args)
categ2stats = collections.defaultdict(lambda: Statistics(mc_args))
continuity2stats = collections.defaultdict(lambda: Statistics(mc_args))
multitokenness2stats = collections.defaultdict(
lambda: Statistics(mc_args))
field_whetherseen2stats = collections.defaultdict(
lambda: Statistics(mc_args)) # dict[(field, bool)] -> stats
field_variantness2stats = collections.defaultdict(
lambda: Statistics(mc_args)) # dict[(field, bool)] -> stats
while self.gold or self.pred:
self.check_eof()
sent_gold = self.gold.popleft()
sent_pred = self.pred.popleft()
sent_gold.absorb_mwes_from_contraction_ranges()
sent_pred.absorb_mwes_from_contraction_ranges()
if self.args.debug:
self.print_debug_pairing(sent_gold, sent_pred)
self.compare_sentences(sent_gold, sent_pred)
categories = self.mwe_categs(sent_gold) | self.mwe_categs(
sent_pred)
mweinfos_gold = sent_gold.mwe_infos().values()
mweinfos_pred = sent_pred.mwe_infos().values()
self.add_to_stats(sent_gold,
base_stats,
mweinfos_gold,
mweinfos_pred,
debug_header="Global:")
for category in list(sorted(categories, key=str)):
g = self.mweinfos_per_categ(mweinfos_gold, category)
p = self.mweinfos_per_categ(mweinfos_pred, category)
self.add_to_stats(sent_gold,
categ2stats[category],
g,
p,
debug_header="Category {}:".format(
category or UNLABELED))
for continuity in [True, False]:
g = self.mweinfo_per_continuity(mweinfos_gold, continuity)
p = self.mweinfo_per_continuity(mweinfos_pred, continuity)
self.add_to_stats(sent_gold,
continuity2stats[continuity],
g,
p,
debug_header="Continuous:"
if continuity else "Discontinuous:")
for multitokenness in [True, False]:
g = self.mweinfo_per_multitokenness(mweinfos_gold,
multitokenness)
p = self.mweinfo_per_multitokenness(mweinfos_pred,
multitokenness)
self.add_to_stats(sent_gold,
multitokenness2stats[multitokenness],
g,
p,
debug_header="{}-token:".format(
"Multi" if multitokenness else "Single"))
if self.args.train_file:
for whetherseen in [True, False]:
g = seen.mweinfo_per_whetherseen(mweinfos_gold, "LEMMA",
whetherseen)
p = seen.mweinfo_per_whetherseen(mweinfos_pred, "LEMMA",
whetherseen)
self.add_to_stats(sent_gold,
field_whetherseen2stats[("LEMMA",
whetherseen)],
g,
p,
debug_header="{}-in-train:".format(
"Seen" if whetherseen else "Unseen"))
for variantness in [True, False]:
# We interpret variantness==False as "MWEs that were seen and are identical"
g = seen.mweinfo_per_variantness(mweinfos_gold, "LEMMA",
"FORM", variantness)
p = seen.mweinfo_per_variantness(mweinfos_pred, "LEMMA",
"FORM", variantness)
self.add_to_stats(
sent_gold,
field_variantness2stats[("LEMMA", "FORM",
variantness)],
g,
p,
debug_header="{}-train:".format(
"Variant-of" if variantness else "Identical-to"))
if self.args.debug:
print("DEBUG:")
#------------------------------------------
print("## Global evaluation")
base_stats.print_stats(prefix='')
print()
print("## Per-category evaluation (partition of Global)")
for category in sorted(categ2stats, key=str):
prefix = '{}: '.format(category or UNLABELED)
categ2stats[category].print_mwebased_proportion(
prefix, baseline=base_stats)
categ2stats[category].print_stats(prefix)
print()
print("## MWE continuity (partition of Global)")
for continuity in [True, False]:
prefix = "Continuous: " if continuity else "Discontinuous: "
continuity2stats[continuity].print_mwebased_proportion(
prefix, baseline=base_stats)
continuity2stats[continuity].c_mwebased.print_p_r_f(prefix)
print()
print("## Number of tokens (partition of Global)")
for multitokenness in [True, False]:
prefix = "{}-token: ".format(
"Multi" if multitokenness else "Single")
multitokenness2stats[multitokenness].print_mwebased_proportion(
prefix, baseline=base_stats)
multitokenness2stats[multitokenness].c_mwebased.print_p_r_f(prefix)
print()
if self.args.train_file:
if not seen.mwe_fieldindex_sets["LEMMA"]:
tsvlib.warn(
"found no MWEs in training file (in field={field_name})",
field_name="LEMMA",
position='')
else:
print("## Whether seen in train (partition of Global)")
for whetherseen in [True, False]:
prefix = "{}-in-train: ".format(
"Seen" if whetherseen else "Unseen")
field_whetherseen2stats[("LEMMA", whetherseen)] \
.print_mwebased_proportion(prefix, baseline=base_stats)
field_whetherseen2stats[(
"LEMMA", whetherseen)].c_mwebased.print_p_r_f(prefix)
print()
print(
"## Whether identical to train (partition of Seen-in-train)"
)
for variantness in [True, False]:
prefix = "{}-train: ".format(
"Variant-of" if variantness else "Identical-to")
field_variantness2stats[("LEMMA", "FORM", variantness)] \
.print_mwebased_proportion(prefix, baseline=field_whetherseen2stats[("LEMMA", True)])
field_variantness2stats[(
"LEMMA", "FORM",
variantness)].c_mwebased.print_p_r_f(prefix)
print()
import argparse
import os
import sys
sys.path.append('.')
import tsvlib
parser = argparse.ArgumentParser(
description='Simple usage example of the tsvlib library')
parser.add_argument("--input",
type=argparse.FileType('r'),
required=True,
help="""Path to input file (in .cupt format)""")
args = parser.parse_args()
with args.input as f:
sentences = list(tsvlib.iter_tsv_sentences(f))
for sentence in sentences:
print("\n-------------------------------")
print("NEW SENTENCE")
forms = " ".join(token['FORM'] for token in sentence.words)
print("Text:", forms)
first = sentence.words[0]
first_LEMMA = first['LEMMA']
first_UPOS = first.get(
'UPOS', '??') # UPOS not necessarily defined for every token...
first_FEATS = first.get(
'FEATS', '??') # FEATS not necessarily defined for every token...
first_HEAD = int(first.get('HEAD', 0))
parent = sentence.words[first_HEAD -
