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refactor fixes (#5664)

Browse Source 
* fixes in ud_train, UX for morphs

* update pyproject with new version of thinc

* fixes in debug_data script

* cleanup of old unused error messages

* remove obsolete TempErrors

* move error messages to errors.py

* add ENT_KB_ID to default DocBin serialization

* few fixes to simple_ner

* fix tags
This commit is contained in:
Sofie Van Landeghem 2020-06-29 14:33:00 +02:00 committed by GitHub
parent fc3cb1fa9e
commit 8d3c0306e1
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GPG Key ID: 4AEE18F83AFDEB23
13 changed files with 68 additions and 139 deletions
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37
bin/ud/ud_train.py
View File

@ -78,8 +78,7 @@ def read_data(
head = int(head) - 1 if head != "0" else id_
sent["words"].append(word)
sent["tags"].append(tag)
sent["morphology"].append(_parse_morph_string(morph))
sent["morphology"][-1].add("POS_%s" % pos)
sent["morphs"].append(_compile_morph_string(morph, pos))
sent["heads"].append(head)
sent["deps"].append("ROOT" if dep == "root" else dep)
sent["spaces"].append(space_after == "_")
@ -88,12 +87,12 @@ def read_data(
if oracle_segments:
docs.append(Doc(nlp.vocab, words=sent["words"], spaces=sent["spaces"]))
golds.append(sent)
assert golds[-1].morphology is not None
assert golds[-1]["morphs"] is not None
sent_annots.append(sent)
if raw_text and max_doc_length and len(sent_annots) >= max_doc_length:
doc, gold = _make_gold(nlp, None, sent_annots)
assert gold.morphology is not None
assert gold["morphs"] is not None
sent_annots = []
docs.append(doc)
golds.append(gold)
@ -109,17 +108,10 @@ def read_data(
return golds_to_gold_data(docs, golds)
def _parse_morph_string(morph_string):
def _compile_morph_string(morph_string, pos):
if morph_string == '_':
return set()
output = []
replacements = {'1': 'one', '2': 'two', '3': 'three'}
for feature in morph_string.split('|'):
key, value = feature.split('=')
value = replacements.get(value, value)
value = value.split(',')[0]
output.append('%s_%s' % (key, value.lower()))
return set(output)
return f"POS={pos}"
return morph_string + f"|POS={pos}"
def read_conllu(file_):
@ -155,7 +147,7 @@ def _make_gold(nlp, text, sent_annots, drop_deps=0.0):
sent_starts = []
for sent in sent_annots:
gold["heads"].extend(len(gold["words"])+head for head in sent["heads"])
for field in ["words", "tags", "deps", "morphology", "entities", "spaces"]:
for field in ["words", "tags", "deps", "morphs", "entities", "spaces"]:
gold[field].extend(sent[field])
sent_starts.append(True)
sent_starts.extend([False] * (len(sent["words"]) - 1))
@ -168,7 +160,7 @@ def _make_gold(nlp, text, sent_annots, drop_deps=0.0):
doc = nlp.make_doc(text)
gold.pop("spaces")
gold["sent_starts"] = sent_starts
for i in range(len(gold.heads)):
for i in range(len(gold["heads"])):
if random.random() < drop_deps:
gold["heads"][i] = None
gold["labels"][i] = None
@ -185,7 +177,7 @@ def golds_to_gold_data(docs, golds):
"""Get out the training data format used by begin_training"""
data = []
for doc, gold in zip(docs, golds):
example = Example.from_dict(doc, gold)
example = Example.from_dict(doc, dict(gold))
data.append(example)
return data
@ -354,8 +346,7 @@ def initialize_pipeline(nlp, examples, config, device):
if config.multitask_sent:
nlp.parser.add_multitask_objective("sent_start")
for eg in examples:
gold = eg.gold
for tag in gold.tags:
for tag in eg.get_aligned("TAG", as_string=True):
if tag is not None:
nlp.tagger.add_label(tag)
if torch is not None and device != -1:
@ -489,10 +480,6 @@ def main(
Token.set_extension("begins_fused", default=False)
Token.set_extension("inside_fused", default=False)
Token.set_extension("get_conllu_lines", method=get_token_conllu)
Token.set_extension("begins_fused", default=False)
Token.set_extension("inside_fused", default=False)
spacy.util.fix_random_seed()
lang.zh.Chinese.Defaults.use_jieba = False
lang.ja.Japanese.Defaults.use_janome = False
@ -535,10 +522,10 @@ def main(
else:
batches = minibatch(examples, size=batch_sizes)
losses = {}
n_train_words = sum(len(eg.doc) for eg in examples)
n_train_words = sum(len(eg.predicted) for eg in examples)
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
for batch in batches:
pbar.update(sum(len(ex.doc) for ex in batch))
pbar.update(sum(len(ex.predicted) for ex in batch))
nlp.parser.cfg["beam_update_prob"] = next(beam_prob)
nlp.update(
batch,

2
examples/training/conllu.py
View File

@ -283,7 +283,7 @@ def initialize_pipeline(nlp, examples, config):
nlp.parser.moves.add_action(2, "subtok")
nlp.add_pipe(nlp.create_pipe("tagger"))
for eg in examples:
for tag in eg.gold.tags:
for tag in eg.get_aligned("TAG", as_string=True):
if tag is not None:
nlp.tagger.add_label(tag)
# Replace labels that didn't make the frequency cutoff

2
examples/training/train_ner.py
View File

@ -56,7 +56,7 @@ def main(model=None, output_dir=None, n_iter=100):
print("Add label", ent[2])
ner.add_label(ent[2])
with nlp.select_pipes(enable="ner") and warnings.catch_warnings():
with nlp.select_pipes(enable="simple_ner") and warnings.catch_warnings():
# show warnings for misaligned entity spans once
warnings.filterwarnings("once", category=UserWarning, module="spacy")

35
spacy/cli/debug_data.py
View File

@ -102,9 +102,6 @@ def debug_data(
corpus = Corpus(train_path, dev_path)
try:
train_dataset = list(corpus.train_dataset(nlp))
train_dataset_unpreprocessed = list(
corpus.train_dataset_without_preprocessing(nlp)
)
except ValueError as e:
loading_train_error_message = f"Training data cannot be loaded: {e}"
try:
@ -120,11 +117,9 @@ def debug_data(
msg.good("Corpus is loadable")
# Create all gold data here to avoid iterating over the train_dataset constantly
gold_train_data = _compile_gold(train_dataset, pipeline, nlp)
gold_train_unpreprocessed_data = _compile_gold(
train_dataset_unpreprocessed, pipeline
)
gold_dev_data = _compile_gold(dev_dataset, pipeline, nlp)
gold_train_data = _compile_gold(train_dataset, pipeline, nlp, make_proj=True)
gold_train_unpreprocessed_data = _compile_gold(train_dataset, pipeline, nlp, make_proj=False)
gold_dev_data = _compile_gold(dev_dataset, pipeline, nlp, make_proj=True)
train_texts = gold_train_data["texts"]
dev_texts = gold_dev_data["texts"]
@ -497,7 +492,7 @@ def _load_file(file_path: Path, msg: Printer) -> None:
def _compile_gold(
examples: Sequence[Example], pipeline: List[str], nlp: Language
examples: Sequence[Example], pipeline: List[str], nlp: Language, make_proj: bool
) -> Dict[str, Any]:
data = {
"ner": Counter(),
@ -517,9 +512,9 @@ def _compile_gold(
"n_cats_multilabel": 0,
"texts": set(),
}
for example in examples:
gold = example.reference
doc = example.predicted
for eg in examples:
gold = eg.reference
doc = eg.predicted
valid_words = [x for x in gold if x is not None]
data["words"].update(valid_words)
data["n_words"] += len(valid_words)
@ -530,7 +525,7 @@ def _compile_gold(
if nlp.vocab.strings[word] not in nlp.vocab.vectors:
data["words_missing_vectors"].update([word])
if "ner" in pipeline:
for i, label in enumerate(gold.ner):
for i, label in enumerate(eg.get_aligned_ner()):
if label is None:
continue
if label.startswith(("B-", "U-", "L-")) and doc[i].is_space:
@ -556,16 +551,18 @@ def _compile_gold(
if list(gold.cats.values()).count(1.0) != 1:
data["n_cats_multilabel"] += 1
if "tagger" in pipeline:
data["tags"].update([x for x in gold.tags if x is not None])
tags = eg.get_aligned("TAG", as_string=True)
data["tags"].update([x for x in tags if x is not None])
if "parser" in pipeline:
data["deps"].update([x for x in gold.labels if x is not None])
for i, (dep, head) in enumerate(zip(gold.labels, gold.heads)):
aligned_heads, aligned_deps = eg.get_aligned_parse(projectivize=make_proj)
data["deps"].update([x for x in aligned_deps if x is not None])
for i, (dep, head) in enumerate(zip(aligned_deps, aligned_heads)):
if head == i:
data["roots"].update([dep])
data["n_sents"] += 1
if nonproj.is_nonproj_tree(gold.heads):
if nonproj.is_nonproj_tree(aligned_heads):
data["n_nonproj"] += 1
if nonproj.contains_cycle(gold.heads):
if nonproj.contains_cycle(aligned_heads):
data["n_cycles"] += 1
return data
@ -581,7 +578,7 @@ def _get_examples_without_label(data: Sequence[Example], label: str) -> int:
for eg in data:
labels = [
label.split("-")[1]
for label in eg.gold.ner
for label in eg.get_aligned_ner()
if label not in ("O", "-", None)
]
if label not in labels:

83
spacy/errors.py
View File

@ -132,6 +132,7 @@ class Warnings(object):
"are currently: da, de, el, en, id, lb, pt, ru, sr, ta, th.")
# TODO: fix numbering after merging develop into master
W092 = ("Ignoring annotations for sentence starts, as dependency heads are set.")
W093 = ("Could not find any data to train the {name} on. Is your "
"input data correctly formatted ?")
W094 = ("Model '{model}' ({model_version}) specifies an under-constrained "
@ -154,7 +155,7 @@ class Warnings(object):
"so a default configuration was used.")
W099 = ("Expected 'dict' type for the 'model' argument of pipe '{pipe}', "
"but got '{type}' instead, so ignoring it.")
W100 = ("Skipping unsupported morphological feature(s): {feature}. "
W100 = ("Skipping unsupported morphological feature(s): '{feature}'. "
"Provide features as a dict {{\"Field1\": \"Value1,Value2\"}} or "
"string \"Field1=Value1,Value2|Field2=Value3\".")
@ -182,18 +183,13 @@ class Errors(object):
"`nlp.select_pipes()`, you should remove them explicitly with "
"`nlp.remove_pipe()` before the pipeline is restored. Names of "
"the new components: {names}")
E009 = ("The `update` method expects same number of docs and golds, but "
"got: {n_docs} docs, {n_golds} golds.")
E010 = ("Word vectors set to length 0. This may be because you don't have "
"a model installed or loaded, or because your model doesn't "
"include word vectors. For more info, see the docs:\n"
"https://spacy.io/usage/models")
E011 = ("Unknown operator: '{op}'. Options: {opts}")
E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
E013 = ("Error selecting action in matcher")
E014 = ("Unknown tag ID: {tag}")
E015 = ("Conflicting morphology exception for ({tag}, {orth}). Use "
"`force=True` to overwrite.")
E016 = ("MultitaskObjective target should be function or one of: dep, "
"tag, ent, dep_tag_offset, ent_tag.")
E017 = ("Can only add unicode or bytes. Got type: {value_type}")
@ -201,21 +197,8 @@ class Errors(object):
"refers to an issue with the `Vocab` or `StringStore`.")
E019 = ("Can't create transition with unknown action ID: {action}. Action "
"IDs are enumerated in spacy/syntax/{src}.pyx.")
E020 = ("Could not find a gold-standard action to supervise the "
"dependency parser. The tree is non-projective (i.e. it has "
"crossing arcs - see spacy/syntax/nonproj.pyx for definitions). "
"The ArcEager transition system only supports projective trees. "
"To learn non-projective representations, transform the data "
"before training and after parsing. Either pass "
"`make_projective=True` to the GoldParse class, or use "
"spacy.syntax.nonproj.preprocess_training_data.")
E021 = ("Could not find a gold-standard action to supervise the "
"dependency parser. The GoldParse was projective. The transition "
"system has {n_actions} actions. State at failure: {state}")
E022 = ("Could not find a transition with the name '{name}' in the NER "
"model.")
E023 = ("Error cleaning up beam: The same state occurred twice at "
"memory address {addr} and position {i}.")
E024 = ("Could not find an optimal move to supervise the parser. Usually, "
"this means that the model can't be updated in a way that's valid "
"and satisfies the correct annotations specified in the GoldParse. "
@ -259,7 +242,6 @@ class Errors(object):
"offset {start}.")
E037 = ("Error calculating span: Can't find a token ending at character "
"offset {end}.")
E038 = ("Error finding sentence for span. Infinite loop detected.")
E039 = ("Array bounds exceeded while searching for root word. This likely "
"means the parse tree is in an invalid state. Please report this "
"issue here: http://github.com/explosion/spaCy/issues")
@ -290,8 +272,6 @@ class Errors(object):
E059 = ("One (and only one) keyword arg must be set. Got: {kwargs}")
E060 = ("Cannot add new key to vectors: the table is full. Current shape: "
"({rows}, {cols}).")
E061 = ("Bad file name: {filename}. Example of a valid file name: "
"'vectors.128.f.bin'")
E062 = ("Cannot find empty bit for new lexical flag. All bits between 0 "
"and 63 are occupied. You can replace one by specifying the "
"`flag_id` explicitly, e.g. "
@ -305,39 +285,17 @@ class Errors(object):
"Query string: {string}\nOrth cached: {orth}\nOrth ID: {orth_id}")
E065 = ("Only one of the vector table's width and shape can be specified. "
"Got width {width} and shape {shape}.")
E066 = ("Error creating model helper for extracting columns. Can only "
"extract columns by positive integer. Got: {value}.")
E067 = ("Invalid BILUO tag sequence: Got a tag starting with 'I' (inside "
"an entity) without a preceding 'B' (beginning of an entity). "
"Tag sequence:\n{tags}")
E068 = ("Invalid BILUO tag: '{tag}'.")
E069 = ("Invalid gold-standard parse tree. Found cycle between word "
"IDs: {cycle} (tokens: {cycle_tokens}) in the document starting "
"with tokens: {doc_tokens}.")
E070 = ("Invalid gold-standard data. Number of documents ({n_docs}) "
"does not align with number of annotations ({n_annots}).")
E071 = ("Error creating lexeme: specified orth ID ({orth}) does not "
"match the one in the vocab ({vocab_orth}).")
E072 = ("Error serializing lexeme: expected data length {length}, "
"got {bad_length}.")
E073 = ("Cannot assign vector of length {new_length}. Existing vectors "
"are of length {length}. You can use `vocab.reset_vectors` to "
"clear the existing vectors and resize the table.")
E074 = ("Error interpreting compiled match pattern: patterns are expected "
"to end with the attribute {attr}. Got: {bad_attr}.")
E075 = ("Error accepting match: length ({length}) > maximum length "
"({max_len}).")
E076 = ("Error setting tensor on Doc: tensor has {rows} rows, while Doc "
"has {words} words.")
E077 = ("Error computing {value}: number of Docs ({n_docs}) does not "
"equal number of GoldParse objects ({n_golds}) in batch.")
E078 = ("Error computing score: number of words in Doc ({words_doc}) does "
"not equal number of words in GoldParse ({words_gold}).")
E079 = ("Error computing states in beam: number of predicted beams "
"({pbeams}) does not equal number of gold beams ({gbeams}).")
E080 = ("Duplicate state found in beam: {key}.")
E081 = ("Error getting gradient in beam: number of histories ({n_hist}) "
"does not equal number of losses ({losses}).")
E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), "
"projective heads ({n_proj_heads}) and labels ({n_labels}) do not "
"match.")
@ -345,8 +303,6 @@ class Errors(object):
"`getter` (plus optional `setter`) is allowed. Got: {nr_defined}")
E084 = ("Error assigning label ID {label} to span: not in StringStore.")
E085 = ("Can't create lexeme for string '{string}'.")
E086 = ("Error deserializing lexeme '{string}': orth ID {orth_id} does "
"not match hash {hash_id} in StringStore.")
E087 = ("Unknown displaCy style: {style}.")
E088 = ("Text of length {length} exceeds maximum of {max_length}. The "
"v2.x parser and NER models require roughly 1GB of temporary "
@ -388,7 +344,6 @@ class Errors(object):
E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A "
"token can only be part of one entity, so make sure the entities "
"you're setting don't overlap.")
E104 = ("Can't find JSON schema for '{name}'.")
E105 = ("The Doc.print_tree() method is now deprecated. Please use "
"Doc.to_json() instead or write your own function.")
E106 = ("Can't find doc._.{attr} attribute specified in the underscore "
@ -411,8 +366,6 @@ class Errors(object):
"practically no advantage over pickling the parent Doc directly. "
"So instead of pickling the span, pickle the Doc it belongs to or "
"use Span.as_doc to convert the span to a standalone Doc object.")
E113 = ("The newly split token can only have one root (head = 0).")
E114 = ("The newly split token needs to have a root (head = 0).")
E115 = ("All subtokens must have associated heads.")
E116 = ("Cannot currently add labels to pretrained text classifier. Add "
"labels before training begins. This functionality was available "
@ -435,12 +388,9 @@ class Errors(object):
"equal to span length ({span_len}).")
E122 = ("Cannot find token to be split. Did it get merged?")
E123 = ("Cannot find head of token to be split. Did it get merged?")
E124 = ("Cannot read from file: {path}. Supported formats: {formats}")
E125 = ("Unexpected value: {value}")
E126 = ("Unexpected matcher predicate: '{bad}'. Expected one of: {good}. "
"This is likely a bug in spaCy, so feel free to open an issue.")
E127 = ("Cannot create phrase pattern representation for length 0. This "
"is likely a bug in spaCy.")
E128 = ("Unsupported serialization argument: '{arg}'. The use of keyword "
"arguments to exclude fields from being serialized or deserialized "
"is now deprecated. Please use the `exclude` argument instead. "
@ -482,8 +432,6 @@ class Errors(object):
"provided {found}.")
E143 = ("Labels for component '{name}' not initialized. Did you forget to "
"call add_label()?")
E144 = ("Could not find parameter `{param}` when building the entity "
"linker model.")
E145 = ("Error reading `{param}` from input file.")
E146 = ("Could not access `{path}`.")
E147 = ("Unexpected error in the {method} functionality of the "
@ -495,8 +443,6 @@ class Errors(object):
"the component matches the model being loaded.")
E150 = ("The language of the `nlp` object and the `vocab` should be the "
"same, but found '{nlp}' and '{vocab}' respectively.")
E151 = ("Trying to call nlp.update without required annotation types. "
"Expected top-level keys: {exp}. Got: {unexp}.")
E152 = ("The attribute {attr} is not supported for token patterns. "
"Please use the option validate=True with Matcher, PhraseMatcher, "
"or EntityRuler for more details.")
@ -533,11 +479,6 @@ class Errors(object):
"that case.")
E166 = ("Can only merge DocBins with the same pre-defined attributes.\n"
"Current DocBin: {current}\nOther DocBin: {other}")
E167 = ("Unknown morphological feature: '{feat}' ({feat_id}). This can "
"happen if the tagger was trained with a different set of "
"morphological features. If you're using a pretrained model, make "
"sure that your models are up to date:\npython -m spacy validate")
E168 = ("Unknown field: {field}")
E169 = ("Can't find module: {module}")
E170 = ("Cannot apply transition {name}: invalid for the current state.")
E171 = ("Matcher.add received invalid on_match callback argument: expected "
@ -548,8 +489,6 @@ class Errors(object):
E173 = ("As of v2.2, the Lemmatizer is initialized with an instance of "
"Lookups containing the lemmatization tables. See the docs for "
"details: https://spacy.io/api/lemmatizer#init")
E174 = ("Architecture '{name}' not found in registry. Available "
"names: {names}")
E175 = ("Can't remove rule for unknown match pattern ID: {key}")
E176 = ("Alias '{alias}' is not defined in the Knowledge Base.")
E177 = ("Ill-formed IOB input detected: {tag}")
@ -597,10 +536,19 @@ class Errors(object):
E198 = ("Unable to return {n} most similar vectors for the current vectors "
"table, which contains {n_rows} vectors.")
E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
E200 = ("Specifying a base model with a pretrained component '{component}' "
"can not be combined with adding a pretrained Tok2Vec layer.")
# TODO: fix numbering after merging develop into master
E971 = ("Found incompatible lengths in Doc.from_array: {array_length} for the "
"array and {doc_length} for the Doc itself.")
E972 = ("Example.__init__ got None for '{arg}'. Requires Doc.")
E973 = ("Unexpected type for NER data")
E974 = ("Unknown {obj} attribute: {key}")
E975 = ("The method Example.from_dict expects a Doc as first argument, "
"but got {type}")
E976 = ("The method Example.from_dict expects a dict as second argument, "
"but received None.")
E977 = ("Can not compare a MorphAnalysis with a string object. "
"This is likely a bug in spaCy, so feel free to open an issue.")
E978 = ("The {method} method of component {name} takes a list of Example objects, "
"but found {types} instead.")
E979 = ("Cannot convert {type} to an Example object.")
@ -648,13 +596,8 @@ class Errors(object):
@add_codes
class TempErrors(object):
T003 = ("Resizing pretrained Tagger models is not currently supported.")
T004 = ("Currently parser depth is hard-coded to 1. Received: {value}.")
T007 = ("Can't yet set {attr} from Span. Vote for this feature on the "
"issue tracker: http://github.com/explosion/spaCy/issues")
T008 = ("Bad configuration of Tagger. This is probably a bug within "
"spaCy. We changed the name of an internal attribute for loading "
"pretrained vectors, and the class has been passed the old name "
"(pretrained_dims) but not the new name (pretrained_vectors).")
# fmt: on

2
spacy/gold/corpus.py
View File

@ -45,7 +45,7 @@ class Corpus:
def make_examples(self, nlp, reference_docs, max_length=0):
for reference in reference_docs:
if max_length >= 1 and len(reference) >= max_length:
if len(reference) >= max_length >= 1:
if reference.is_sentenced:
for ref_sent in reference.sents:
yield Example(

24
spacy/gold/example.pyx
View File

@ -2,7 +2,6 @@ import warnings
import numpy
from ..tokens import Token
from ..tokens.doc cimport Doc
from ..tokens.span cimport Span
from ..tokens.span import Span
@ -11,9 +10,8 @@ from .align cimport Alignment
from .iob_utils import biluo_to_iob, biluo_tags_from_offsets, biluo_tags_from_doc
from .iob_utils import spans_from_biluo_tags
from .align import Alignment
from ..errors import Errors, AlignmentError
from ..errors import Errors, Warnings
from ..syntax import nonproj
from ..util import get_words_and_spaces
cpdef Doc annotations2doc(vocab, tok_annot, doc_annot):
@ -32,11 +30,10 @@ cpdef Doc annotations2doc(vocab, tok_annot, doc_annot):
cdef class Example:
def __init__(self, Doc predicted, Doc reference, *, Alignment alignment=None):
""" Doc can either be text, or an actual Doc """
msg = "Example.__init__ got None for '{arg}'. Requires Doc."
if predicted is None:
raise TypeError(msg.format(arg="predicted"))
raise TypeError(Errors.E972.format(arg="predicted"))
if reference is None:
raise TypeError(msg.format(arg="reference"))
raise TypeError(Errors.E972.format(arg="reference"))
self.x = predicted
self.y = reference
self._alignment = alignment
@ -64,9 +61,9 @@ cdef class Example:
@classmethod
def from_dict(cls, Doc predicted, dict example_dict):
if example_dict is None:
raise ValueError("Example.from_dict expected dict, received None")
raise ValueError(Errors.E976)
if not isinstance(predicted, Doc):
raise TypeError(f"Argument 1 should be Doc. Got {type(predicted)}")
raise TypeError(Errors.E975.format(type=type(predicted)))
example_dict = _fix_legacy_dict_data(example_dict)
tok_dict, doc_dict = _parse_example_dict_data(example_dict)
if "ORTH" not in tok_dict:
@ -118,7 +115,8 @@ cdef class Example:
aligned_deps = [None] * self.x.length
heads = [token.head.i for token in self.y]
deps = [token.dep_ for token in self.y]
heads, deps = nonproj.projectivize(heads, deps)
if projectivize:
heads, deps = nonproj.projectivize(heads, deps)
for cand_i in range(self.x.length):
gold_i = cand_to_gold[cand_i]
if gold_i is not None: # Alignment found
@ -245,11 +243,11 @@ def _annot2array(vocab, tok_annot, doc_annot):
elif key == "cats":
pass
else:
raise ValueError(f"Unknown doc attribute: {key}")
raise ValueError(Errors.E974.format(obj="doc", key=key))
for key, value in tok_annot.items():
if key not in IDS:
raise ValueError(f"Unknown token attribute: {key}")
raise ValueError(Errors.E974.format(obj="token", key=key))
elif key in ["ORTH", "SPACY"]:
pass
elif key == "HEAD":
@ -289,7 +287,7 @@ def _add_entities_to_doc(doc, ner_data):
doc.ents = ner_data
doc.ents = [span for span in ner_data if span.label_]
else:
raise ValueError("Unexpected type for NER data")
raise ValueError(Errors.E973)
def _parse_example_dict_data(example_dict):
@ -341,7 +339,7 @@ def _fix_legacy_dict_data(example_dict):
if "HEAD" in token_dict and "SENT_START" in token_dict:
# If heads are set, we don't also redundantly specify SENT_START.
token_dict.pop("SENT_START")
warnings.warn("Ignoring annotations for sentence starts, as dependency heads are set")
warnings.warn(Warnings.W092)
return {
"token_annotation": token_dict,
"doc_annotation": doc_dict

2
spacy/gold/gold_io.pyx
View File

@ -145,7 +145,7 @@ def json_to_annotations(doc):
example["doc_annotation"] = dict(
cats=cats,
entities=ner_tags,
links=paragraph.get("links", []) # TODO: fix/test
links=paragraph.get("links", [])
)
yield example

4
spacy/morphology.pyx
View File

@ -107,9 +107,9 @@ cdef class Morphology:
Returns the hash of the new analysis.
"""
cdef MorphAnalysisC* tag_ptr
if features == self.EMPTY_MORPH:
features = ""
if isinstance(features, str):
if features == self.EMPTY_MORPH:
features = ""
tag_ptr = <MorphAnalysisC*>self.tags.get(<hash_t>self.strings[features])
if tag_ptr != NULL:
return tag_ptr.key

9
spacy/pipeline/simple_ner.py
View File

@ -70,7 +70,7 @@ class SimpleNER(Pipe):
def update(self, examples, set_annotations=False, drop=0.0, sgd=None, losses=None):
if not any(_has_ner(eg) for eg in examples):
return 0
docs = [eg.doc for eg in examples]
docs = [eg.predicted for eg in examples]
set_dropout_rate(self.model, drop)
scores, bp_scores = self.model.begin_update(docs)
loss, d_scores = self.get_loss(examples, scores)
@ -89,7 +89,8 @@ class SimpleNER(Pipe):
d_scores = []
truths = []
for eg in examples:
gold_tags = [(tag if tag != "-" else None) for tag in eg.gold.ner]
tags = eg.get_aligned("TAG", as_string=True)
gold_tags = [(tag if tag != "-" else None) for tag in tags]
if not self.is_biluo:
gold_tags = biluo_to_iob(gold_tags)
truths.append(gold_tags)
@ -128,8 +129,8 @@ class SimpleNER(Pipe):
pass
def _has_ner(eg):
for ner_tag in eg.gold.ner:
def _has_ner(example):
for ner_tag in example.get_aligned_ner():
if ner_tag != "-" and ner_tag is not None:
return True
else:

2
spacy/tokens/_serialize.py
View File

@ -9,7 +9,7 @@ from ..attrs import SPACY, ORTH, intify_attr
from ..errors import Errors
ALL_ATTRS = ("ORTH", "TAG", "HEAD", "DEP", "ENT_IOB", "ENT_TYPE", "LEMMA", "MORPH")
ALL_ATTRS = ("ORTH", "TAG", "HEAD", "DEP", "ENT_IOB", "ENT_TYPE", "ENT_KB_ID", "LEMMA", "MORPH")
class DocBin(object):

2
spacy/tokens/doc.pyx
View File

@ -816,7 +816,7 @@ cdef class Doc:
cdef TokenC* tokens = self.c
cdef int length = len(array)
if length != len(self):
raise ValueError("Cannot set array values longer than the document.")
raise ValueError(Errors.E971.format(array_length=length, doc_length=len(self)))
# Get set up for fast loading
cdef Pool mem = Pool()

3
spacy/tokens/morphanalysis.pyx
View File

@ -1,6 +1,7 @@
from libc.string cimport memset
cimport numpy as np
from ..errors import Errors
from ..vocab cimport Vocab
from ..typedefs cimport hash_t, attr_t
from ..morphology cimport list_features, check_feature, get_by_field
@ -49,6 +50,8 @@ cdef class MorphAnalysis:
return self.key
def __eq__(self, other):
if isinstance(other, str):
raise ValueError(Errors.E977)
return self.key == other.key
def __ne__(self, other):