mirror of
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8661218fe8
* Work on refactoring greedy parser * Compile updated parser * Fix refactored parser * Update test * Fix refactored parser * Fix refactored parser * Readd beam search after refactor * Fix beam search after refactor * Fix parser * Fix beam parsing * Support oracle segmentation in ud-train CLI command * Avoid relying on final gold check in beam search * Add a keyword argument sink to GoldParse * Bug fixes to beam search after refactor * Avoid importing fused token symbol in ud-run-test, untl that's added * Avoid importing fused token symbol in ud-run-test, untl that's added * Don't modify Token in global scope * Fix error in beam gradient calculation * Default to beam_update_prob 1 * Set a more aggressive threshold on the max violn update * Disable some tests to figure out why CI fails * Disable some tests to figure out why CI fails * Add some diagnostics to travis.yml to try to figure out why build fails * Tell Thinc to link against system blas on Travis * Point thinc to libblas on Travis * Try running sudo=true for travis * Unhack travis.sh * Restore beam_density argument for parser beam * Require thinc 6.11.1.dev16 * Revert hacks to tests * Revert hacks to travis.yml * Update thinc requirement * Fix parser model loading * Fix size limits in training data * Add missing name attribute for parser * Fix appveyor for Windows
316 lines
10 KiB
Python
316 lines
10 KiB
Python
'''Train for CONLL 2017 UD treebank evaluation. Takes .conllu files, writes
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.conllu format for development data, allowing the official scorer to be used.
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'''
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from __future__ import unicode_literals
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import plac
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import tqdm
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from pathlib import Path
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import re
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import sys
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import json
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import spacy
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import spacy.util
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from ..tokens import Token, Doc
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from ..gold import GoldParse
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from ..util import compounding, minibatch_by_words
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from ..syntax.nonproj import projectivize
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from ..matcher import Matcher
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#from ..morphology import Fused_begin, Fused_inside
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from .. import displacy
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from collections import defaultdict, Counter
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from timeit import default_timer as timer
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Fused_begin = None
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Fused_inside = None
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import itertools
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import random
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import numpy.random
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import cytoolz
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from . import conll17_ud_eval
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from .. import lang
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from .. import lang
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from ..lang import zh
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from ..lang import ja
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from ..lang import ru
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################
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# Data reading #
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################
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space_re = re.compile('\s+')
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def split_text(text):
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return [space_re.sub(' ', par.strip()) for par in text.split('\n\n')]
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##############
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# Evaluation #
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##############
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def read_conllu(file_):
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docs = []
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sent = []
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doc = []
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for line in file_:
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if line.startswith('# newdoc'):
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if doc:
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docs.append(doc)
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doc = []
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elif line.startswith('#'):
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continue
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elif not line.strip():
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if sent:
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doc.append(sent)
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sent = []
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else:
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sent.append(list(line.strip().split('\t')))
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if len(sent[-1]) != 10:
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print(repr(line))
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raise ValueError
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if sent:
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doc.append(sent)
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if doc:
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docs.append(doc)
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return docs
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def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
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if text_loc.parts[-1].endswith('.conllu'):
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docs = []
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with text_loc.open() as file_:
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for conllu_doc in read_conllu(file_):
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for conllu_sent in conllu_doc:
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words = [line[1] for line in conllu_sent]
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docs.append(Doc(nlp.vocab, words=words))
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for name, component in nlp.pipeline:
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docs = list(component.pipe(docs))
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else:
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with text_loc.open('r', encoding='utf8') as text_file:
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texts = split_text(text_file.read())
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docs = list(nlp.pipe(texts))
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with sys_loc.open('w', encoding='utf8') as out_file:
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write_conllu(docs, out_file)
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with gold_loc.open('r', encoding='utf8') as gold_file:
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gold_ud = conll17_ud_eval.load_conllu(gold_file)
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with sys_loc.open('r', encoding='utf8') as sys_file:
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sys_ud = conll17_ud_eval.load_conllu(sys_file)
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scores = conll17_ud_eval.evaluate(gold_ud, sys_ud)
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return docs, scores
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def write_conllu(docs, file_):
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merger = Matcher(docs[0].vocab)
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merger.add('SUBTOK', None, [{'DEP': 'subtok', 'op': '+'}])
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for i, doc in enumerate(docs):
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matches = merger(doc)
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spans = [doc[start:end+1] for _, start, end in matches]
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offsets = [(span.start_char, span.end_char) for span in spans]
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for start_char, end_char in offsets:
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doc.merge(start_char, end_char)
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# TODO: This shuldn't be necessary? Should be handled in merge
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for word in doc:
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if word.i == word.head.i:
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word.dep_ = 'ROOT'
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file_.write("# newdoc id = {i}\n".format(i=i))
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for j, sent in enumerate(doc.sents):
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file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))
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file_.write("# text = {text}\n".format(text=sent.text))
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for k, token in enumerate(sent):
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file_.write(_get_token_conllu(token, k, len(sent)) + '\n')
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file_.write('\n')
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for word in sent:
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if word.head.i == word.i and word.dep_ == 'ROOT':
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break
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else:
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print("Rootless sentence!")
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print(sent)
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print(i)
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for w in sent:
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print(w.i, w.text, w.head.text, w.head.i, w.dep_)
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raise ValueError
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def _get_token_conllu(token, k, sent_len):
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if token.check_morph(Fused_begin) and (k+1 < sent_len):
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n = 1
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text = [token.text]
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while token.nbor(n).check_morph(Fused_inside):
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text.append(token.nbor(n).text)
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n += 1
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id_ = '%d-%d' % (k+1, (k+n))
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fields = [id_, ''.join(text)] + ['_'] * 8
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lines = ['\t'.join(fields)]
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else:
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lines = []
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if token.head.i == token.i:
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head = 0
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else:
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head = k + (token.head.i - token.i) + 1
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fields = [str(k+1), token.text, token.lemma_, token.pos_, token.tag_, '_',
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str(head), token.dep_.lower(), '_', '_']
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if token.check_morph(Fused_begin) and (k+1 < sent_len):
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if k == 0:
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fields[1] = token.norm_[0].upper() + token.norm_[1:]
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else:
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fields[1] = token.norm_
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elif token.check_morph(Fused_inside):
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fields[1] = token.norm_
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elif token._.split_start is not None:
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split_start = token._.split_start
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split_end = token._.split_end
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split_len = (split_end.i - split_start.i) + 1
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n_in_split = token.i - split_start.i
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subtokens = guess_fused_orths(split_start.text, [''] * split_len)
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fields[1] = subtokens[n_in_split]
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lines.append('\t'.join(fields))
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return '\n'.join(lines)
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def guess_fused_orths(word, ud_forms):
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'''The UD data 'fused tokens' don't necessarily expand to keys that match
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the form. We need orths that exact match the string. Here we make a best
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effort to divide up the word.'''
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if word == ''.join(ud_forms):
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# Happy case: we get a perfect split, with each letter accounted for.
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return ud_forms
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elif len(word) == sum(len(subtoken) for subtoken in ud_forms):
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# Unideal, but at least lengths match.
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output = []
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remain = word
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for subtoken in ud_forms:
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assert len(subtoken) >= 1
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output.append(remain[:len(subtoken)])
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remain = remain[len(subtoken):]
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assert len(remain) == 0, (word, ud_forms, remain)
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return output
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else:
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# Let's say word is 6 long, and there are three subtokens. The orths
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# *must* equal the original string. Arbitrarily, split [4, 1, 1]
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first = word[:len(word)-(len(ud_forms)-1)]
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output = [first]
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remain = word[len(first):]
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for i in range(1, len(ud_forms)):
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assert remain
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output.append(remain[:1])
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remain = remain[1:]
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assert len(remain) == 0, (word, output, remain)
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return output
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def print_results(name, ud_scores):
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fields = {}
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if ud_scores is not None:
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fields.update({
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'words': ud_scores['Words'].f1 * 100,
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'sents': ud_scores['Sentences'].f1 * 100,
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'tags': ud_scores['XPOS'].f1 * 100,
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'uas': ud_scores['UAS'].f1 * 100,
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'las': ud_scores['LAS'].f1 * 100,
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})
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else:
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fields.update({
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'words': 0.0,
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'sents': 0.0,
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'tags': 0.0,
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'uas': 0.0,
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'las': 0.0
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})
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tpl = '\t'.join((
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name,
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'{las:.1f}',
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'{uas:.1f}',
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'{tags:.1f}',
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'{sents:.1f}',
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'{words:.1f}',
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))
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print(tpl.format(**fields))
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return fields
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def get_token_split_start(token):
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if token.text == '':
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assert token.i != 0
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i = -1
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while token.nbor(i).text == '':
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i -= 1
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return token.nbor(i)
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elif (token.i+1) < len(token.doc) and token.nbor(1).text == '':
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return token
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else:
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return None
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def get_token_split_end(token):
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if (token.i+1) == len(token.doc):
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return token if token.text == '' else None
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elif token.text != '' and token.nbor(1).text != '':
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return None
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i = 1
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while (token.i+i) < len(token.doc) and token.nbor(i).text == '':
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i += 1
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return token.nbor(i-1)
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##################
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# Initialization #
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##################
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def load_nlp(experiments_dir, corpus):
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nlp = spacy.load(experiments_dir / corpus / 'best-model')
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return nlp
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def initialize_pipeline(nlp, docs, golds, config, device):
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nlp.add_pipe(nlp.create_pipe('parser'))
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return nlp
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@plac.annotations(
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test_data_dir=("Path to Universal Dependencies test data", "positional", None, Path),
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experiment_dir=("Parent directory with output model", "positional", None, Path),
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corpus=("UD corpus to evaluate, e.g. UD_English, UD_Spanish, etc", "positional", None, str),
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)
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def main(test_data_dir, experiment_dir, corpus):
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Token.set_extension('split_start', getter=get_token_split_start)
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Token.set_extension('split_end', getter=get_token_split_end)
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Token.set_extension('begins_fused', default=False)
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Token.set_extension('inside_fused', default=False)
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lang.zh.Chinese.Defaults.use_jieba = False
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lang.ja.Japanese.Defaults.use_janome = False
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lang.ru.Russian.Defaults.use_pymorphy2 = False
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nlp = load_nlp(experiment_dir, corpus)
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treebank_code = nlp.meta['treebank']
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for section in ('test', 'dev'):
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if section == 'dev':
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section_dir = 'conll17-ud-development-2017-03-19'
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else:
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section_dir = 'conll17-ud-test-2017-05-09'
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text_path = test_data_dir / 'input' / section_dir / (treebank_code+'.txt')
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udpipe_path = test_data_dir / 'input' / section_dir / (treebank_code+'-udpipe.conllu')
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gold_path = test_data_dir / 'gold' / section_dir / (treebank_code+'.conllu')
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header = [section, 'LAS', 'UAS', 'TAG', 'SENT', 'WORD']
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print('\t'.join(header))
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inputs = {'gold': gold_path, 'udp': udpipe_path, 'raw': text_path}
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for input_type in ('udp', 'raw'):
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input_path = inputs[input_type]
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output_path = experiment_dir / corpus / '{section}.conllu'.format(section=section)
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parsed_docs, test_scores = evaluate(nlp, input_path, gold_path, output_path)
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accuracy = print_results(input_type, test_scores)
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acc_path = experiment_dir / corpus / '{section}-accuracy.json'.format(section=section)
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with open(acc_path, 'w') as file_:
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file_.write(json.dumps(accuracy, indent=2))
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if __name__ == '__main__':
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plac.call(main)
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