spaCy/spacy/cli/ud/ud_run_test.py
Ines Montani 5651a0d052 💫 Replace {Doc,Span}.merge with Doc.retokenize (#3280)
* Add deprecation warning to Doc.merge and Span.merge

* Replace {Doc,Span}.merge with Doc.retokenize
2019-02-15 10:29:44 +01:00

339 lines
10 KiB
Python

# flake8: noqa
"""Train for CONLL 2017 UD treebank evaluation. Takes .conllu files, writes
.conllu format for development data, allowing the official scorer to be used.
"""
from __future__ import unicode_literals
import plac
import tqdm
from pathlib import Path
import re
import sys
import srsly
import spacy
import spacy.util
from ...tokens import Token, Doc
from ...gold import GoldParse
from ...util import compounding, minibatch_by_words
from ...syntax.nonproj import projectivize
from ...matcher import Matcher
# from ...morphology import Fused_begin, Fused_inside
from ... import displacy
from collections import defaultdict, Counter
from timeit import default_timer as timer
Fused_begin = None
Fused_inside = None
import itertools
import random
import numpy.random
from . import conll17_ud_eval
from ... import lang
from ...lang import zh
from ...lang import ja
from ...lang import ru
################
# Data reading #
################
space_re = re.compile(r"\s+")
def split_text(text):
return [space_re.sub(" ", par.strip()) for par in text.split("\n\n")]
##############
# Evaluation #
##############
def read_conllu(file_):
docs = []
sent = []
doc = []
for line in file_:
if line.startswith("# newdoc"):
if doc:
docs.append(doc)
doc = []
elif line.startswith("#"):
continue
elif not line.strip():
if sent:
doc.append(sent)
sent = []
else:
sent.append(list(line.strip().split("\t")))
if len(sent[-1]) != 10:
print(repr(line))
raise ValueError
if sent:
doc.append(sent)
if doc:
docs.append(doc)
return docs
def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
if text_loc.parts[-1].endswith(".conllu"):
docs = []
with text_loc.open() as file_:
for conllu_doc in read_conllu(file_):
for conllu_sent in conllu_doc:
words = [line[1] for line in conllu_sent]
docs.append(Doc(nlp.vocab, words=words))
for name, component in nlp.pipeline:
docs = list(component.pipe(docs))
else:
with text_loc.open("r", encoding="utf8") as text_file:
texts = split_text(text_file.read())
docs = list(nlp.pipe(texts))
with sys_loc.open("w", encoding="utf8") as out_file:
write_conllu(docs, out_file)
with gold_loc.open("r", encoding="utf8") as gold_file:
gold_ud = conll17_ud_eval.load_conllu(gold_file)
with sys_loc.open("r", encoding="utf8") as sys_file:
sys_ud = conll17_ud_eval.load_conllu(sys_file)
scores = conll17_ud_eval.evaluate(gold_ud, sys_ud)
return docs, scores
def write_conllu(docs, file_):
merger = Matcher(docs[0].vocab)
merger.add("SUBTOK", None, [{"DEP": "subtok", "op": "+"}])
for i, doc in enumerate(docs):
matches = merger(doc)
spans = [doc[start : end + 1] for _, start, end in matches]
with doc.retokenize() as retokenizer:
for span in spans:
retokenizer.merge(span)
# TODO: This shouldn't be necessary? Should be handled in merge
for word in doc:
if word.i == word.head.i:
word.dep_ = "ROOT"
file_.write("# newdoc id = {i}\n".format(i=i))
for j, sent in enumerate(doc.sents):
file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))
file_.write("# text = {text}\n".format(text=sent.text))
for k, token in enumerate(sent):
file_.write(_get_token_conllu(token, k, len(sent)) + "\n")
file_.write("\n")
for word in sent:
if word.head.i == word.i and word.dep_ == "ROOT":
break
else:
print("Rootless sentence!")
print(sent)
print(i)
for w in sent:
print(w.i, w.text, w.head.text, w.head.i, w.dep_)
raise ValueError
def _get_token_conllu(token, k, sent_len):
if token.check_morph(Fused_begin) and (k + 1 < sent_len):
n = 1
text = [token.text]
while token.nbor(n).check_morph(Fused_inside):
text.append(token.nbor(n).text)
n += 1
id_ = "%d-%d" % (k + 1, (k + n))
fields = [id_, "".join(text)] + ["_"] * 8
lines = ["\t".join(fields)]
else:
lines = []
if token.head.i == token.i:
head = 0
else:
head = k + (token.head.i - token.i) + 1
fields = [
str(k + 1),
token.text,
token.lemma_,
token.pos_,
token.tag_,
"_",
str(head),
token.dep_.lower(),
"_",
"_",
]
if token.check_morph(Fused_begin) and (k + 1 < sent_len):
if k == 0:
fields[1] = token.norm_[0].upper() + token.norm_[1:]
else:
fields[1] = token.norm_
elif token.check_morph(Fused_inside):
fields[1] = token.norm_
elif token._.split_start is not None:
split_start = token._.split_start
split_end = token._.split_end
split_len = (split_end.i - split_start.i) + 1
n_in_split = token.i - split_start.i
subtokens = guess_fused_orths(split_start.text, [""] * split_len)
fields[1] = subtokens[n_in_split]
lines.append("\t".join(fields))
return "\n".join(lines)
def guess_fused_orths(word, ud_forms):
"""The UD data 'fused tokens' don't necessarily expand to keys that match
the form. We need orths that exact match the string. Here we make a best
effort to divide up the word."""
if word == "".join(ud_forms):
# Happy case: we get a perfect split, with each letter accounted for.
return ud_forms
elif len(word) == sum(len(subtoken) for subtoken in ud_forms):
# Unideal, but at least lengths match.
output = []
remain = word
for subtoken in ud_forms:
assert len(subtoken) >= 1
output.append(remain[: len(subtoken)])
remain = remain[len(subtoken) :]
assert len(remain) == 0, (word, ud_forms, remain)
return output
else:
# Let's say word is 6 long, and there are three subtokens. The orths
# *must* equal the original string. Arbitrarily, split [4, 1, 1]
first = word[: len(word) - (len(ud_forms) - 1)]
output = [first]
remain = word[len(first) :]
for i in range(1, len(ud_forms)):
assert remain
output.append(remain[:1])
remain = remain[1:]
assert len(remain) == 0, (word, output, remain)
return output
def print_results(name, ud_scores):
fields = {}
if ud_scores is not None:
fields.update(
{
"words": ud_scores["Words"].f1 * 100,
"sents": ud_scores["Sentences"].f1 * 100,
"tags": ud_scores["XPOS"].f1 * 100,
"uas": ud_scores["UAS"].f1 * 100,
"las": ud_scores["LAS"].f1 * 100,
}
)
else:
fields.update({"words": 0.0, "sents": 0.0, "tags": 0.0, "uas": 0.0, "las": 0.0})
tpl = "\t".join(
(name, "{las:.1f}", "{uas:.1f}", "{tags:.1f}", "{sents:.1f}", "{words:.1f}")
)
print(tpl.format(**fields))
return fields
def get_token_split_start(token):
if token.text == "":
assert token.i != 0
i = -1
while token.nbor(i).text == "":
i -= 1
return token.nbor(i)
elif (token.i + 1) < len(token.doc) and token.nbor(1).text == "":
return token
else:
return None
def get_token_split_end(token):
if (token.i + 1) == len(token.doc):
return token if token.text == "" else None
elif token.text != "" and token.nbor(1).text != "":
return None
i = 1
while (token.i + i) < len(token.doc) and token.nbor(i).text == "":
i += 1
return token.nbor(i - 1)
##################
# Initialization #
##################
def load_nlp(experiments_dir, corpus):
nlp = spacy.load(experiments_dir / corpus / "best-model")
return nlp
def initialize_pipeline(nlp, docs, golds, config, device):
nlp.add_pipe(nlp.create_pipe("parser"))
return nlp
@plac.annotations(
test_data_dir=(
"Path to Universal Dependencies test data",
"positional",
None,
Path,
),
experiment_dir=("Parent directory with output model", "positional", None, Path),
corpus=(
"UD corpus to evaluate, e.g. UD_English, UD_Spanish, etc",
"positional",
None,
str,
),
)
def main(test_data_dir, experiment_dir, corpus):
Token.set_extension("split_start", getter=get_token_split_start)
Token.set_extension("split_end", getter=get_token_split_end)
Token.set_extension("begins_fused", default=False)
Token.set_extension("inside_fused", default=False)
lang.zh.Chinese.Defaults.use_jieba = False
lang.ja.Japanese.Defaults.use_janome = False
lang.ru.Russian.Defaults.use_pymorphy2 = False
nlp = load_nlp(experiment_dir, corpus)
treebank_code = nlp.meta["treebank"]
for section in ("test", "dev"):
if section == "dev":
section_dir = "conll17-ud-development-2017-03-19"
else:
section_dir = "conll17-ud-test-2017-05-09"
text_path = test_data_dir / "input" / section_dir / (treebank_code + ".txt")
udpipe_path = (
test_data_dir / "input" / section_dir / (treebank_code + "-udpipe.conllu")
)
gold_path = test_data_dir / "gold" / section_dir / (treebank_code + ".conllu")
header = [section, "LAS", "UAS", "TAG", "SENT", "WORD"]
print("\t".join(header))
inputs = {"gold": gold_path, "udp": udpipe_path, "raw": text_path}
for input_type in ("udp", "raw"):
input_path = inputs[input_type]
output_path = (
experiment_dir / corpus / "{section}.conllu".format(section=section)
)
parsed_docs, test_scores = evaluate(nlp, input_path, gold_path, output_path)
accuracy = print_results(input_type, test_scores)
acc_path = (
experiment_dir
/ corpus
/ "{section}-accuracy.json".format(section=section)
)
srsly.write_json(acc_path, accuracy)
if __name__ == "__main__":
plac.call(main)