spaCy/spacy/gold/iob_utils.py

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Improve spacy.gold (no GoldParse, no json format!) (#5555) * Update errors * Remove beam for now (maybe) Remove beam_utils Update setup.py Remove beam * Remove GoldParse WIP on removing goldparse Get ArcEager compiling after GoldParse excise Update setup.py Get spacy.syntax compiling after removing GoldParse Rename NewExample -> Example and clean up Clean html files Start updating tests Update Morphologizer * fix error numbers * fix merge conflict * informative error when calling to_array with wrong field * fix error catching * fixing language and scoring tests * start testing get_aligned * additional tests for new get_aligned function * Draft create_gold_state for arc_eager oracle * Fix import * Fix import * Remove TokenAnnotation code from nonproj * fixing NER one-to-many alignment * Fix many-to-one IOB codes * fix test for misaligned * attempt to fix cases with weird spaces * fix spaces * test_gold_biluo_different_tokenization works * allow None as BILUO annotation * fixed some tests + WIP roundtrip unit test * add spaces to json output format * minibatch utiltiy can deal with strings, docs or examples * fix augment (needs further testing) * various fixes in scripts - needs to be further tested * fix test_cli * cleanup * correct silly typo * add support for MORPH in to/from_array, fix morphologizer overfitting test * fix tagger * fix entity linker * ensure test keeps working with non-linked entities * pipe() takes docs, not examples * small bug fix * textcat bugfix * throw informative error when running the components with the wrong type of objects * fix parser tests to work with example (most still failing) * fix BiluoPushDown parsing entities * small fixes * bugfix tok2vec * fix renames and simple_ner labels * various small fixes * prevent writing dummy values like deps because that could interfer with sent_start values * fix the fix * implement split_sent with aligned SENT_START attribute * test for split sentences with various alignment issues, works * Return ArcEagerGoldParse from ArcEager * Update parser and NER gold stuff * Draft new GoldCorpus class * add links to to_dict * clean up * fix test checking for variants * Fix oracles * Start updating converters * Move converters under spacy.gold * Move things around * Fix naming * Fix name * Update converter to produce DocBin * Update converters * Allow DocBin to take list of Doc objects. * Make spacy convert output docbin * Fix import * Fix docbin * Fix compile in ArcEager * Fix import * Serialize all attrs by default * Update converter * Remove jsonl converter * Add json2docs converter * Draft Corpus class for DocBin * Work on train script * Update Corpus * Update DocBin * Allocate Doc before starting to add words * Make doc.from_array several times faster * Update train.py * Fix Corpus * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests * Skip tests that cause crashes * Skip test causing segfault * Remove GoldCorpus * Update imports * Update after removing GoldCorpus * Fix module name of corpus * Fix mimport * Work on parser oracle * Update arc_eager oracle * Restore ArcEager.get_cost function * Update transition system * Update test_arc_eager_oracle * Remove beam test * Update test * Unskip * Unskip tests * add links to to_dict * clean up * fix test checking for variants * Allow DocBin to take list of Doc objects. * Fix compile in ArcEager * Serialize all attrs by default Move converters under spacy.gold Move things around Fix naming Fix name Update converter to produce DocBin Update converters Make spacy convert output docbin Fix import Fix docbin Fix import Update converter Remove jsonl converter Add json2docs converter * Allocate Doc before starting to add words * Make doc.from_array several times faster * Start updating converters * Work on train script * Draft Corpus class for DocBin Update Corpus Fix Corpus * Update DocBin Add missing strings when serializing * Update train.py * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests Skip tests that cause crashes Skip test causing segfault * Remove GoldCorpus Update imports Update after removing GoldCorpus Fix module name of corpus Fix mimport * Work on parser oracle Update arc_eager oracle Restore ArcEager.get_cost function Update transition system * Update tests Remove beam test Update test Unskip Unskip tests * Add get_aligned_parse method in Example Fix Example.get_aligned_parse * Add kwargs to Corpus.dev_dataset to match train_dataset * Update nonproj * Use get_aligned_parse in ArcEager * Add another arc-eager oracle test * Remove Example.doc property Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc * Update ArcEager oracle Fix Break oracle * Debugging * Fix Corpus * Fix eg.doc * Format * small fixes * limit arg for Corpus * fix test_roundtrip_docs_to_docbin * fix test_make_orth_variants * fix add_label test * Update tests * avoid writing temp dir in json2docs, fixing 4402 test * Update test * Add missing costs to NER oracle * Update test * Work on Example.get_aligned_ner method * Clean up debugging * Xfail tests * Remove prints * Remove print * Xfail some tests * Replace unseen labels for parser * Update test * Update test * Xfail test * Fix Corpus * fix imports * fix docs_to_json * various small fixes * cleanup * Support gold_preproc in Corpus * Support gold_preproc * Pass gold_preproc setting into corpus * Remove debugging * Fix gold_preproc * Fix json2docs converter * Fix convert command * Fix flake8 * Fix import * fix output_dir (converted to Path by typer) * fix var * bugfix: update states after creating golds to avoid out of bounds indexing * Improve efficiency of ArEager oracle * pull merge_sent into iob2docs to avoid Doc creation for each line * fix asserts * bugfix excl Span.end in iob2docs * Support max_length in Corpus * Fix arc_eager oracle * Filter out uannotated sentences in NER * Remove debugging in parser * Simplify NER alignment * Fix conversion of NER data * Fix NER init_gold_batch * Tweak efficiency of precomputable affine * Update onto-json default * Update gold test for NER * Fix parser test * Update test * Add NER data test * Fix convert for single file * Fix test * Hack scorer to avoid evaluating non-nered data * Fix handling of NER data in Example * Output unlabelled spans from O biluo tags in iob_utils * Fix unset variable * Return kept examples from init_gold_batch * Return examples from init_gold_batch * Dont return Example from init_gold_batch * Set spaces on gold doc after conversion * Add test * Fix spaces reading * Improve NER alignment * Improve handling of missing values in NER * Restore the 'cutting' in parser training * Add assertion * Print epochs * Restore random cuts in parser/ner training * Implement Doc.copy * Implement Example.copy * Copy examples at the start of Language.update * Don't unset example docs * Tweak parser model slightly * attempt to fix _guess_spaces * _add_entities_to_doc first, so that links don't get overwritten * fixing get_aligned_ner for one-to-many * fix indexing into x_text * small fix biluo_tags_from_offsets * Add onto-ner config * Simplify NER alignment * Fix NER scoring for partially annotated documents * fix indexing into x_text * fix test_cli failing tests by ignoring spans in doc.ents with empty label * Fix limit * Improve NER alignment * Fix count_train * Remove print statement * fix tests, we're not having nothing but None * fix clumsy fingers * Fix tests * Fix doc.ents * Remove empty docs in Corpus and improve limit * Update config Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
2020-06-26 20:34:12 +03:00
import warnings
from ..errors import Errors, Warnings
from ..tokens import Span
def iob_to_biluo(tags):
out = []
tags = list(tags)
while tags:
out.extend(_consume_os(tags))
out.extend(_consume_ent(tags))
return out
def biluo_to_iob(tags):
out = []
for tag in tags:
if tag is None:
out.append(tag)
else:
tag = tag.replace("U-", "B-", 1).replace("L-", "I-", 1)
out.append(tag)
return out
def _consume_os(tags):
while tags and tags[0] == "O":
yield tags.pop(0)
def _consume_ent(tags):
if not tags:
return []
tag = tags.pop(0)
target_in = "I" + tag[1:]
target_last = "L" + tag[1:]
length = 1
while tags and tags[0] in {target_in, target_last}:
length += 1
tags.pop(0)
label = tag[2:]
if length == 1:
if len(label) == 0:
raise ValueError(Errors.E177.format(tag=tag))
return ["U-" + label]
else:
start = "B-" + label
end = "L-" + label
middle = [f"I-{label}" for _ in range(1, length - 1)]
return [start] + middle + [end]
def biluo_tags_from_doc(doc, missing="O"):
return biluo_tags_from_offsets(
doc,
[(ent.start_char, ent.end_char, ent.label_) for ent in doc.ents],
missing=missing,
)
def biluo_tags_from_offsets(doc, entities, missing="O"):
"""Encode labelled spans into per-token tags, using the
Begin/In/Last/Unit/Out scheme (BILUO).
doc (Doc): The document that the entity offsets refer to. The output tags
will refer to the token boundaries within the document.
entities (iterable): A sequence of `(start, end, label)` triples. `start`
and `end` should be character-offset integers denoting the slice into
the original string.
RETURNS (list): A list of unicode strings, describing the tags. Each tag
string will be of the form either "", "O" or "{action}-{label}", where
action is one of "B", "I", "L", "U". The string "-" is used where the
entity offsets don't align with the tokenization in the `Doc` object.
The training algorithm will view these as missing values. "O" denotes a
non-entity token. "B" denotes the beginning of a multi-token entity,
"I" the inside of an entity of three or more tokens, and "L" the end
of an entity of two or more tokens. "U" denotes a single-token entity.
EXAMPLE:
>>> text = 'I like London.'
>>> entities = [(len('I like '), len('I like London'), 'LOC')]
>>> doc = nlp.tokenizer(text)
>>> tags = biluo_tags_from_offsets(doc, entities)
>>> assert tags == ["O", "O", 'U-LOC', "O"]
"""
# Ensure no overlapping entity labels exist
tokens_in_ents = {}
starts = {token.idx: token.i for token in doc}
ends = {token.idx + len(token): token.i for token in doc}
biluo = ["-" for _ in doc]
# Handle entity cases
for start_char, end_char, label in entities:
if not label:
for s in starts: # account for many-to-one
if s >= start_char and s < end_char:
biluo[starts[s]] = "O"
else:
for token_index in range(start_char, end_char):
if token_index in tokens_in_ents.keys():
raise ValueError(
Errors.E103.format(
span1=(
tokens_in_ents[token_index][0],
tokens_in_ents[token_index][1],
tokens_in_ents[token_index][2],
),
span2=(start_char, end_char, label),
)
)
tokens_in_ents[token_index] = (start_char, end_char, label)
start_token = starts.get(start_char)
end_token = ends.get(end_char)
# Only interested if the tokenization is correct
if start_token is not None and end_token is not None:
if start_token == end_token:
biluo[start_token] = f"U-{label}"
else:
biluo[start_token] = f"B-{label}"
for i in range(start_token + 1, end_token):
biluo[i] = f"I-{label}"
biluo[end_token] = f"L-{label}"
# Now distinguish the O cases from ones where we miss the tokenization
entity_chars = set()
for start_char, end_char, label in entities:
for i in range(start_char, end_char):
entity_chars.add(i)
for token in doc:
for i in range(token.idx, token.idx + len(token)):
if i in entity_chars:
break
else:
biluo[token.i] = missing
if "-" in biluo and missing != "-":
ent_str = str(entities)
warnings.warn(
Warnings.W030.format(
text=doc.text[:50] + "..." if len(doc.text) > 50 else doc.text,
entities=ent_str[:50] + "..." if len(ent_str) > 50 else ent_str,
)
)
return biluo
def spans_from_biluo_tags(doc, tags):
"""Encode per-token tags following the BILUO scheme into Span object, e.g.
to overwrite the doc.ents.
doc (Doc): The document that the BILUO tags refer to.
entities (iterable): A sequence of BILUO tags with each tag describing one
token. Each tags string will be of the form of either "", "O" or
"{action}-{label}", where action is one of "B", "I", "L", "U".
RETURNS (list): A sequence of Span objects.
"""
token_offsets = tags_to_entities(tags)
spans = []
for label, start_idx, end_idx in token_offsets:
span = Span(doc, start_idx, end_idx + 1, label=label)
spans.append(span)
return spans
def offsets_from_biluo_tags(doc, tags):
"""Encode per-token tags following the BILUO scheme into entity offsets.
doc (Doc): The document that the BILUO tags refer to.
entities (iterable): A sequence of BILUO tags with each tag describing one
token. Each tags string will be of the form of either "", "O" or
"{action}-{label}", where action is one of "B", "I", "L", "U".
RETURNS (list): A sequence of `(start, end, label)` triples. `start` and
`end` will be character-offset integers denoting the slice into the
original string.
"""
spans = spans_from_biluo_tags(doc, tags)
return [(span.start_char, span.end_char, span.label_) for span in spans]
def tags_to_entities(tags):
""" Note that the end index returned by this function is inclusive.
To use it for Span creation, increment the end by 1."""
entities = []
start = None
for i, tag in enumerate(tags):
if tag is None:
continue
if tag.startswith("O"):
# TODO: We shouldn't be getting these malformed inputs. Fix this.
if start is not None:
start = None
else:
entities.append(("", i, i))
continue
elif tag == "-":
continue
elif tag.startswith("I"):
if start is None:
raise ValueError(Errors.E067.format(tags=tags[: i + 1]))
continue
if tag.startswith("U"):
entities.append((tag[2:], i, i))
elif tag.startswith("B"):
start = i
elif tag.startswith("L"):
entities.append((tag[2:], start, i))
start = None
else:
raise ValueError(Errors.E068.format(tag=tag))
return entities