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Fix Example details for train CLI / pipeline components (#4624)

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* Switch to train_dataset() function in train CLI

* Fixes for pipe() methods in pipeline components

* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`

* Update Parser.pipe() for Examples

* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s

* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)

* Fixes to Example implementation in spacy.gold

* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`

* Head of 0 are not treated as unset

* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)

* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example

* Don't clobber `examples` variable in `iter_gold_docs()`

* Add/modify gold tests for handling projectivity

* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)

* Add test for projective train vs. nonprojective dev versions of the
same `Doc`

* Handle ignore_misaligned as arg rather than attr

Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.

Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).

* Remove unused attrs from gold.pxd

Remove `ignore_misaligned` and `make_projective` from `gold.pxd`

* Refer to Example.goldparse in iter_gold_docs()

Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError

* Update test for ignore_misaligned
This commit is contained in:
adrianeboyd 2019-11-23 14:32:15 +01:00 committed by Matthew Honnibal
parent faaa832518
commit 44829950ba
6 changed files with 179 additions and 74 deletions
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2
spacy/cli/train.py
View File

@ -340,7 +340,7 @@ def train(
iter_since_best = 0
best_score = 0.0
for i in range(n_iter):
train_data = corpus.train_data(
train_data = corpus.train_dataset(
nlp,
noise_level=noise_level,
orth_variant_level=orth_variant_level,

2
spacy/gold.pxd
View File

@ -58,8 +58,6 @@ cdef class Example:
cdef public object doc
cdef public list token_annotations
cdef public DocAnnotation doc_annotation
cdef public object make_projective
cdef public object ignore_misaligned
cdef public object goldparse

61
spacy/gold.pyx
View File

@ -311,47 +311,50 @@ class GoldCorpus(object):
ignore_misaligned=ignore_misaligned)
yield from gold_examples
def train_dataset_without_preprocessing(self, nlp, gold_preproc=False):
examples = self.iter_gold_docs(nlp, self.train_examples, gold_preproc=gold_preproc)
def train_dataset_without_preprocessing(self, nlp, gold_preproc=False,
ignore_misaligned=False):
examples = self.iter_gold_docs(nlp, self.train_examples,
gold_preproc=gold_preproc,
ignore_misaligned=ignore_misaligned)
yield from examples
def dev_dataset(self, nlp, gold_preproc=False, ignore_misaligned=False):
examples = self.iter_gold_docs(nlp, self.dev_examples, gold_preproc=gold_preproc,
ignore_misaligned=ignore_misaligned)
examples = self.iter_gold_docs(nlp, self.dev_examples,
gold_preproc=gold_preproc,
ignore_misaligned=ignore_misaligned)
yield from examples
@classmethod
def iter_gold_docs(cls, nlp, examples, gold_preproc, max_length=None,
noise_level=0.0, orth_variant_level=0.0, make_projective=False,
ignore_misaligned=False):
noise_level=0.0, orth_variant_level=0.0,
make_projective=False, ignore_misaligned=False):
""" Setting gold_preproc will result in creating a doc per 'sentence' """
for example in examples:
if gold_preproc:
example.doc = None
else:
example = example.merge_sents()
example.make_projective = make_projective
example.ignore_misaligned = ignore_misaligned
examples = cls._make_docs(nlp, example,
example_docs = cls._make_docs(nlp, example,
gold_preproc, noise_level=noise_level,
orth_variant_level=orth_variant_level)
examples = cls._make_golds(examples, vocab=nlp.vocab)
for ex in examples:
if ex.gold is not None:
example_golds = cls._make_golds(example_docs, vocab=nlp.vocab,
make_projective=make_projective,
ignore_misaligned=ignore_misaligned)
for ex in example_golds:
if ex.goldparse is not None:
if (not max_length) or len(ex.doc) < max_length:
yield ex
@classmethod
def _make_docs(cls, nlp, example, gold_preproc, noise_level=0.0, orth_variant_level=0.0):
var_example = make_orth_variants(nlp, example, orth_variant_level=orth_variant_level)
# gold_preproc is not used ?!
if example.text is not None:
var_example = make_orth_variants(nlp, example, orth_variant_level=orth_variant_level)
var_text = add_noise(var_example.text, noise_level)
var_doc = nlp.make_doc(var_text)
var_example.doc = var_doc
return [var_example]
else:
var_example = make_orth_variants(nlp, example, orth_variant_level=orth_variant_level)
doc_examples = []
for token_annotation in var_example.token_annotations:
t_doc = Doc(nlp.vocab, words=add_noise(token_annotation.words, noise_level))
@ -362,10 +365,13 @@ class GoldCorpus(object):
return doc_examples
@classmethod
def _make_golds(cls, examples, vocab=None):
def _make_golds(cls, examples, vocab=None, make_projective=False,
ignore_misaligned=False):
gold_examples = []
for example in examples:
gold_parses = example.get_gold_parses(vocab=vocab)
gold_parses = example.get_gold_parses(vocab=vocab,
make_projective=make_projective,
ignore_misaligned=ignore_misaligned)
for (doc, gold) in gold_parses:
ex = Example(doc=doc)
ex.goldparse = gold
@ -693,13 +699,11 @@ cdef class DocAnnotation:
cdef class Example:
def __init__(self, doc_annotation=None, token_annotations=None, doc=None,
make_projective=False, ignore_misaligned=False, goldparse=None):
goldparse=None):
""" Doc can either be text, or an actual Doc """
self.doc = doc
self.doc_annotation = doc_annotation if doc_annotation else DocAnnotation()
self.token_annotations = token_annotations if token_annotations else []
self.make_projective = make_projective
self.ignore_misaligned = ignore_misaligned
self.goldparse = goldparse
@classmethod
@ -760,7 +764,7 @@ cdef class Example:
m_ids.extend(id_ + i for id_ in t.ids)
m_words.extend(t.words)
m_tags.extend(t.tags)
m_heads.extend(head + i if head else None for head in t.heads)
m_heads.extend(head + i if head is not None and head >= 0 else head_i + i for head_i, head in enumerate(t.heads))
m_deps.extend(t.deps)
m_ents.extend(t.entities)
m_morph.extend(t.morphology)
@ -773,7 +777,8 @@ cdef class Example:
return m_example
def get_gold_parses(self, merge=False, vocab=None):
def get_gold_parses(self, merge=False, vocab=None, make_projective=False,
ignore_misaligned=False):
"""Return a list of (doc, GoldParse) objects.
If merge is set to True, add all Token annotations to one big list."""
d = self.doc_annotation
@ -788,20 +793,20 @@ cdef class Example:
raise ValueError(Errors.E998)
m_doc = Doc(vocab, words=t.words)
try:
gp = GoldParse.from_annotation(m_doc, d, t, make_projective=self.make_projective)
gp = GoldParse.from_annotation(m_doc, d, t, make_projective=make_projective)
except AlignmentError:
if self.ignore_misaligned:
if ignore_misaligned:
gp = None
else:
raise
return [(self.doc, gp)]
# we only have one sentence and an appropriate doc
elif len(self.token_annotations) == 1 and self.doc is not None:
elif len(self.token_annotations) == 1 and isinstance(self.doc, Doc):
t = self.token_annotations[0]
try:
gp = GoldParse.from_annotation(self.doc, d, t, make_projective=self.make_projective)
gp = GoldParse.from_annotation(self.doc, d, t, make_projective=make_projective)
except AlignmentError:
if self.ignore_misaligned:
if ignore_misaligned:
gp = None
else:
raise
@ -814,9 +819,9 @@ cdef class Example:
raise ValueError(Errors.E998)
t_doc = Doc(vocab, words=t.words)
try:
gp = GoldParse.from_annotation(t_doc, d, t, make_projective=self.make_projective)
gp = GoldParse.from_annotation(t_doc, d, t, make_projective=make_projective)
except AlignmentError:
if self.ignore_misaligned:
if ignore_misaligned:
gp = None
else:
raise

36
spacy/pipeline/pipes.pyx
View File

@ -61,7 +61,7 @@ class Pipe(object):
return cls(nlp.vocab, **cfg)
def _get_doc(self, example):
""" Use this method if the `example` method can be both a Doc or an Example """
""" Use this method if the `example` can be both a Doc or an Example """
if isinstance(example, Doc):
return example
return example.doc
@ -102,7 +102,6 @@ class Pipe(object):
and `set_annotations()` methods.
"""
for examples in util.minibatch(stream, size=batch_size):
examples = list(examples)
docs = [self._get_doc(ex) for ex in examples]
predictions = self.predict(docs)
if isinstance(predictions, tuple) and len(tuple) == 2:
@ -112,11 +111,11 @@ class Pipe(object):
self.set_annotations(docs, predictions)
if as_example:
examples = []
annotated_examples = []
for ex, doc in zip(examples, docs):
ex.doc = doc
examples.append(ex)
yield from examples
annotated_examples.append(ex)
yield from annotated_examples
else:
yield from docs
@ -312,11 +311,11 @@ class Tensorizer(Pipe):
self.set_annotations(docs, tensors)
if as_example:
examples = []
annotated_examples = []
for ex, doc in zip(examples, docs):
ex.doc = doc
examples.append(ex)
yield from examples
annotated_examples.append(ex)
yield from annotated_examples
else:
yield from docs
@ -434,17 +433,16 @@ class Tagger(Pipe):
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
for examples in util.minibatch(stream, size=batch_size):
examples = list(examples)
docs = [self._get_doc(ex) for ex in examples]
tag_ids, tokvecs = self.predict(docs)
self.set_annotations(docs, tag_ids, tensors=tokvecs)
if as_example:
examples = []
annotated_examples = []
for ex, doc in zip(examples, docs):
ex.doc = doc
examples.append(ex)
yield from examples
annotated_examples.append(ex)
yield from annotated_examples
else:
yield from docs
@ -1000,17 +998,16 @@ class TextCategorizer(Pipe):
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
for examples in util.minibatch(stream, size=batch_size):
examples = list(examples)
docs = [self._get_doc(ex) for ex in examples]
scores, tensors = self.predict(docs)
self.set_annotations(docs, scores, tensors=tensors)
if as_example:
examples = []
annotated_examples = []
for ex, doc in zip(examples, docs):
ex.doc = doc
examples.append(ex)
yield from examples
annotated_examples.append(ex)
yield from annotated_examples
else:
yield from docs
@ -1333,17 +1330,16 @@ class EntityLinker(Pipe):
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
for examples in util.minibatch(stream, size=batch_size):
examples = list(examples)
docs = [self._get_doc(ex) for ex in examples]
kb_ids, tensors = self.predict(docs)
self.set_annotations(docs, kb_ids, tensors=tensors)
if as_example:
examples = []
annotated_examples = []
for ex, doc in zip(examples, docs):
ex.doc = doc
examples.append(ex)
yield from examples
annotated_examples.append(ex)
yield from annotated_examples
else:
yield from docs

22
spacy/syntax/nn_parser.pyx
View File

@ -227,7 +227,8 @@ cdef class Parser:
self.set_annotations([doc], states, tensors=None)
return doc
def pipe(self, docs, int batch_size=256, int n_threads=-1, beam_width=None):
def pipe(self, docs, int batch_size=256, int n_threads=-1, beam_width=None,
as_example=False):
"""Process a stream of documents.
stream: The sequence of documents to process.
@ -240,14 +241,21 @@ cdef class Parser:
cdef Doc doc
for batch in util.minibatch(docs, size=batch_size):
batch_in_order = list(batch)
by_length = sorted(batch_in_order, key=lambda doc: len(doc))
docs = [self._get_doc(ex) for ex in batch_in_order]
by_length = sorted(docs, key=lambda doc: len(doc))
for subbatch in util.minibatch(by_length, size=max(batch_size//4, 2)):
subbatch = list(subbatch)
parse_states = self.predict(subbatch, beam_width=beam_width,
beam_density=beam_density)
self.set_annotations(subbatch, parse_states, tensors=None)
for doc in batch_in_order:
yield doc
if as_example:
annotated_examples = []
for ex, doc in zip(batch_in_order, docs):
ex.doc = doc
annotated_examples.append(ex)
yield from annotated_examples
else:
yield from batch_in_order
def require_model(self):
"""Raise an error if the component's model is not initialized."""
@ -635,6 +643,12 @@ cdef class Parser:
self.cfg.update(cfg)
return sgd
def _get_doc(self, example):
""" Use this method if the `example` can be both a Doc or an Example """
if isinstance(example, Doc):
return example
return example.doc
def to_disk(self, path, exclude=tuple(), **kwargs):
serializers = {
'model': lambda p: (self.model.to_disk(p) if self.model is not True else True),

130
spacy/tests/test_gold.py
View File

@ -1,16 +1,40 @@
# coding: utf-8
from __future__ import unicode_literals
import spacy
from spacy.errors import AlignmentError
from spacy.gold import biluo_tags_from_offsets, offsets_from_biluo_tags, Example, DocAnnotation
from spacy.gold import spans_from_biluo_tags, GoldParse, iob_to_biluo
from spacy.gold import GoldCorpus, docs_to_json, align
from spacy.lang.en import English
from spacy.syntax.nonproj import is_nonproj_tree
from spacy.tokens import Doc
from spacy.util import compounding, minibatch
from .util import make_tempdir
import pytest
import srsly
@pytest.fixture
def doc():
text = "Sarah's sister flew to Silicon Valley via London."
tags = ['NNP', 'POS', 'NN', 'VBD', 'IN', 'NNP', 'NNP', 'IN', 'NNP', '.']
# head of '.' is intentionally nonprojective for testing
heads = [2, 0, 3, 3, 3, 6, 4, 3, 7, 5]
deps = ['poss', 'case', 'nsubj', 'ROOT', 'prep', 'compound', 'pobj', 'prep', 'pobj', 'punct']
biluo_tags = ["U-PERSON", "O", "O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
cats = {"TRAVEL": 1.0, "BAKING": 0.0}
nlp = English()
doc = nlp(text)
for i in range(len(tags)):
doc[i].tag_ = tags[i]
doc[i].dep_ = deps[i]
doc[i].head = doc[heads[i]]
doc.ents = spans_from_biluo_tags(doc, biluo_tags)
doc.cats = cats
doc.is_tagged = True
doc.is_parsed = True
return doc
def test_gold_biluo_U(en_vocab):
words = ["I", "flew", "to", "London", "."]
@ -98,23 +122,14 @@ def test_iob_to_biluo():
iob_to_biluo(bad_iob)
def test_roundtrip_docs_to_json():
text = "I flew to Silicon Valley via London."
tags = ["PRP", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
heads = [1, 1, 1, 4, 2, 1, 5, 1]
deps = ["nsubj", "ROOT", "prep", "compound", "pobj", "prep", "pobj", "punct"]
biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
cats = {"TRAVEL": 1.0, "BAKING": 0.0}
def test_roundtrip_docs_to_json(doc):
nlp = English()
doc = nlp(text)
for i in range(len(tags)):
doc[i].tag_ = tags[i]
doc[i].dep_ = deps[i]
doc[i].head = doc[heads[i]]
doc.ents = spans_from_biluo_tags(doc, biluo_tags)
doc.cats = cats
doc.is_tagged = True
doc.is_parsed = True
text = doc.text
tags = [t.tag_ for t in doc]
deps = [t.dep_ for t in doc]
heads = [t.head.i for t in doc]
biluo_tags = iob_to_biluo([t.ent_iob_ + "-" + t.ent_type_ if t.ent_type_ else "O" for t in doc])
cats = doc.cats
# roundtrip to JSON
with make_tempdir() as tmpdir:
@ -122,7 +137,7 @@ def test_roundtrip_docs_to_json():
srsly.write_json(json_file, [docs_to_json(doc)])
goldcorpus = GoldCorpus(train=str(json_file), dev=str(json_file))
reloaded_example = next(goldcorpus.train_dataset(nlp))
reloaded_example = next(goldcorpus.dev_dataset(nlp))
goldparse = reloaded_example.gold
assert len(doc) == goldcorpus.count_train()
@ -142,7 +157,7 @@ def test_roundtrip_docs_to_json():
srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
reloaded_example = next(goldcorpus.train_dataset(nlp))
reloaded_example = next(goldcorpus.dev_dataset(nlp))
goldparse = reloaded_example.gold
assert len(doc) == goldcorpus.count_train()
@ -166,7 +181,7 @@ def test_roundtrip_docs_to_json():
srsly.write_jsonl(jsonl_file, goldcorpus.train_examples)
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
reloaded_example = next(goldcorpus.train_dataset(nlp))
reloaded_example = next(goldcorpus.dev_dataset(nlp))
goldparse = reloaded_example.gold
assert len(doc) == goldcorpus.count_train()
@ -181,6 +196,83 @@ def test_roundtrip_docs_to_json():
assert cats["BAKING"] == goldparse.cats["BAKING"]
def test_projective_train_vs_nonprojective_dev(doc):
nlp = English()
text = doc.text
deps = [t.dep_ for t in doc]
heads = [t.head.i for t in doc]
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "test.jsonl"
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
train_reloaded_example = next(goldcorpus.train_dataset(nlp))
train_goldparse = train_reloaded_example.gold
dev_reloaded_example = next(goldcorpus.dev_dataset(nlp))
dev_goldparse = dev_reloaded_example.gold
assert is_nonproj_tree([t.head.i for t in doc]) is True
assert is_nonproj_tree(train_goldparse.heads) is False
assert heads[:-1] == train_goldparse.heads[:-1]
assert heads[-1] != train_goldparse.heads[-1]
assert deps[:-1] == train_goldparse.labels[:-1]
assert deps[-1] != train_goldparse.labels[-1]
assert heads == dev_goldparse.heads
assert deps == dev_goldparse.labels
def test_ignore_misaligned(doc):
nlp = English()
text = doc.text
deps = [t.dep_ for t in doc]
heads = [t.head.i for t in doc]
use_new_align = spacy.gold.USE_NEW_ALIGN
spacy.gold.USE_NEW_ALIGN = False
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "test.jsonl"
data = [docs_to_json(doc)]
data[0]["paragraphs"][0]["raw"] = text.replace("Sarah", "Jane")
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, data)
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
train_reloaded_example = next(goldcorpus.train_dataset(nlp))
spacy.gold.USE_NEW_ALIGN = True
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "test.jsonl"
data = [docs_to_json(doc)]
data[0]["paragraphs"][0]["raw"] = text.replace("Sarah", "Jane")
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, data)
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
with pytest.raises(AlignmentError):
train_reloaded_example = next(goldcorpus.train_dataset(nlp))
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "test.jsonl"
data = [docs_to_json(doc)]
data[0]["paragraphs"][0]["raw"] = text.replace("Sarah", "Jane")
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, data)
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
# doesn't raise an AlignmentError, but there is nothing to iterate over
# because the only example can't be aligned
train_reloaded_example = list(goldcorpus.train_dataset(nlp,
ignore_misaligned=True))
assert len(train_reloaded_example) == 0
spacy.gold.USE_NEW_ALIGN = use_new_align
# xfail while we have backwards-compatible alignment
@pytest.mark.xfail
@pytest.mark.parametrize(