# cython: profile=True # coding: utf8 from __future__ import unicode_literals, print_function import re import random import numpy import tempfile import shutil import itertools from pathlib import Path import srsly from .syntax import nonproj from .tokens import Doc, Span from .errors import Errors, AlignmentError, user_warning, Warnings from .compat import path2str from . import util from .util import minibatch, itershuffle from libc.stdio cimport FILE, fopen, fclose, fread, fwrite, feof, fseek USE_NEW_ALIGN = False punct_re = re.compile(r"\W") def tags_to_entities(tags): 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 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 def merge_sents(sents): m_deps = [[], [], [], [], [], []] m_cats = {} m_brackets = [] i = 0 for (ids, words, tags, heads, labels, ner), (cats, brackets) in sents: m_deps[0].extend(id_ + i for id_ in ids) m_deps[1].extend(words) m_deps[2].extend(tags) m_deps[3].extend(head + i for head in heads) m_deps[4].extend(labels) m_deps[5].extend(ner) m_brackets.extend((b["first"] + i, b["last"] + i, b["label"]) for b in brackets) m_cats.update(cats) i += len(ids) return [(m_deps, (m_cats, m_brackets))] _ALIGNMENT_NORM_MAP = [("``", "'"), ("''", "'"), ('"', "'"), ("`", "'")] def _normalize_for_alignment(tokens): tokens = [w.replace(" ", "").lower() for w in tokens] output = [] for token in tokens: token = token.replace(" ", "").lower() for before, after in _ALIGNMENT_NORM_MAP: token = token.replace(before, after) output.append(token) return output def _align_before_v2_2_2(tokens_a, tokens_b): """Calculate alignment tables between two tokenizations, using the Levenshtein algorithm. The alignment is case-insensitive. tokens_a (List[str]): The candidate tokenization. tokens_b (List[str]): The reference tokenization. RETURNS: (tuple): A 5-tuple consisting of the following information: * cost (int): The number of misaligned tokens. * a2b (List[int]): Mapping of indices in `tokens_a` to indices in `tokens_b`. For instance, if `a2b[4] == 6`, that means that `tokens_a[4]` aligns to `tokens_b[6]`. If there's no one-to-one alignment for a token, it has the value -1. * b2a (List[int]): The same as `a2b`, but mapping the other direction. * a2b_multi (Dict[int, int]): A dictionary mapping indices in `tokens_a` to indices in `tokens_b`, where multiple tokens of `tokens_a` align to the same token of `tokens_b`. * b2a_multi (Dict[int, int]): As with `a2b_multi`, but mapping the other direction. """ from . import _align if tokens_a == tokens_b: alignment = numpy.arange(len(tokens_a)) return 0, alignment, alignment, {}, {} tokens_a = [w.replace(" ", "").lower() for w in tokens_a] tokens_b = [w.replace(" ", "").lower() for w in tokens_b] cost, i2j, j2i, matrix = _align.align(tokens_a, tokens_b) i2j_multi, j2i_multi = _align.multi_align(i2j, j2i, [len(w) for w in tokens_a], [len(w) for w in tokens_b]) for i, j in list(i2j_multi.items()): if i2j_multi.get(i+1) != j and i2j_multi.get(i-1) != j: i2j[i] = j i2j_multi.pop(i) for j, i in list(j2i_multi.items()): if j2i_multi.get(j+1) != i and j2i_multi.get(j-1) != i: j2i[j] = i j2i_multi.pop(j) return cost, i2j, j2i, i2j_multi, j2i_multi def align(tokens_a, tokens_b): """Calculate alignment tables between two tokenizations. tokens_a (List[str]): The candidate tokenization. tokens_b (List[str]): The reference tokenization. RETURNS: (tuple): A 5-tuple consisting of the following information: * cost (int): The number of misaligned tokens. * a2b (List[int]): Mapping of indices in `tokens_a` to indices in `tokens_b`. For instance, if `a2b[4] == 6`, that means that `tokens_a[4]` aligns to `tokens_b[6]`. If there's no one-to-one alignment for a token, it has the value -1. * b2a (List[int]): The same as `a2b`, but mapping the other direction. * a2b_multi (Dict[int, int]): A dictionary mapping indices in `tokens_a` to indices in `tokens_b`, where multiple tokens of `tokens_a` align to the same token of `tokens_b`. * b2a_multi (Dict[int, int]): As with `a2b_multi`, but mapping the other direction. """ if not USE_NEW_ALIGN: return _align_before_v2_2_2(tokens_a, tokens_b) tokens_a = _normalize_for_alignment(tokens_a) tokens_b = _normalize_for_alignment(tokens_b) cost = 0 a2b = numpy.empty(len(tokens_a), dtype="i") b2a = numpy.empty(len(tokens_b), dtype="i") a2b.fill(-1) b2a.fill(-1) a2b_multi = {} b2a_multi = {} i = 0 j = 0 offset_a = 0 offset_b = 0 while i < len(tokens_a) and j < len(tokens_b): a = tokens_a[i][offset_a:] b = tokens_b[j][offset_b:] if a == b: if offset_a == offset_b == 0: a2b[i] = j b2a[j] = i elif offset_a == 0: cost += 2 a2b_multi[i] = j elif offset_b == 0: cost += 2 b2a_multi[j] = i offset_a = offset_b = 0 i += 1 j += 1 elif a == "": assert offset_a == 0 cost += 1 i += 1 elif b == "": assert offset_b == 0 cost += 1 j += 1 elif b.startswith(a): cost += 1 if offset_a == 0: a2b_multi[i] = j i += 1 offset_a = 0 offset_b += len(a) elif a.startswith(b): cost += 1 if offset_b == 0: b2a_multi[j] = i j += 1 offset_b = 0 offset_a += len(b) else: assert "".join(tokens_a) != "".join(tokens_b) raise AlignmentError(Errors.E186.format(tok_a=tokens_a, tok_b=tokens_b)) return cost, a2b, b2a, a2b_multi, b2a_multi class GoldCorpus(object): """An annotated corpus, using the JSON file format. Manages annotations for tagging, dependency parsing and NER. DOCS: https://spacy.io/api/goldcorpus """ def __init__(self, train, dev, gold_preproc=False, limit=None): """Create a GoldCorpus. train_path (unicode or Path): File or directory of training data. dev_path (unicode or Path): File or directory of development data. RETURNS (GoldCorpus): The newly created object. """ self.limit = limit if isinstance(train, str) or isinstance(train, Path): train = self.read_tuples(self.walk_corpus(train)) dev = self.read_tuples(self.walk_corpus(dev)) # Write temp directory with one doc per file, so we can shuffle and stream self.tmp_dir = Path(tempfile.mkdtemp()) self.write_msgpack(self.tmp_dir / "train", train, limit=self.limit) self.write_msgpack(self.tmp_dir / "dev", dev, limit=self.limit) def __del__(self): shutil.rmtree(path2str(self.tmp_dir)) @staticmethod def write_msgpack(directory, doc_tuples, limit=0): if not directory.exists(): directory.mkdir() n = 0 for i, doc_tuple in enumerate(doc_tuples): srsly.write_msgpack(directory / "{}.msg".format(i), [doc_tuple]) n += len(doc_tuple[1]) if limit and n >= limit: break @staticmethod def walk_corpus(path): path = util.ensure_path(path) if not path.is_dir(): return [path] paths = [path] locs = [] seen = set() for path in paths: if str(path) in seen: continue seen.add(str(path)) if path.parts[-1].startswith("."): continue elif path.is_dir(): paths.extend(path.iterdir()) elif path.parts[-1].endswith((".json", ".jsonl")): locs.append(path) return locs @staticmethod def read_tuples(locs, limit=0): i = 0 for loc in locs: loc = util.ensure_path(loc) if loc.parts[-1].endswith("json"): gold_tuples = read_json_file(loc) elif loc.parts[-1].endswith("jsonl"): gold_tuples = srsly.read_jsonl(loc) first_gold_tuple = next(gold_tuples) gold_tuples = itertools.chain([first_gold_tuple], gold_tuples) # TODO: proper format checks with schemas if isinstance(first_gold_tuple, dict): gold_tuples = read_json_object(gold_tuples) elif loc.parts[-1].endswith("msg"): gold_tuples = srsly.read_msgpack(loc) else: supported = ("json", "jsonl", "msg") raise ValueError(Errors.E124.format(path=path2str(loc), formats=supported)) for item in gold_tuples: yield item i += len(item[1]) if limit and i >= limit: return @property def dev_tuples(self): locs = (self.tmp_dir / "dev").iterdir() yield from self.read_tuples(locs, limit=self.limit) @property def train_tuples(self): locs = (self.tmp_dir / "train").iterdir() yield from self.read_tuples(locs, limit=self.limit) def count_train(self): n = 0 i = 0 for raw_text, paragraph_tuples in self.train_tuples: for sent_tuples, brackets in paragraph_tuples: n += len(sent_tuples[1]) if self.limit and i >= self.limit: break i += 1 return n def train_docs(self, nlp, gold_preproc=False, max_length=None, noise_level=0.0, orth_variant_level=0.0, ignore_misaligned=False): locs = list((self.tmp_dir / 'train').iterdir()) random.shuffle(locs) train_tuples = self.read_tuples(locs, limit=self.limit) gold_docs = self.iter_gold_docs(nlp, train_tuples, gold_preproc, max_length=max_length, noise_level=noise_level, orth_variant_level=orth_variant_level, make_projective=True, ignore_misaligned=ignore_misaligned) yield from gold_docs def train_docs_without_preprocessing(self, nlp, gold_preproc=False): gold_docs = self.iter_gold_docs(nlp, self.train_tuples, gold_preproc=gold_preproc) yield from gold_docs def dev_docs(self, nlp, gold_preproc=False, ignore_misaligned=False): gold_docs = self.iter_gold_docs(nlp, self.dev_tuples, gold_preproc=gold_preproc, ignore_misaligned=ignore_misaligned) yield from gold_docs @classmethod def iter_gold_docs(cls, nlp, tuples, gold_preproc, max_length=None, noise_level=0.0, orth_variant_level=0.0, make_projective=False, ignore_misaligned=False): for raw_text, paragraph_tuples in tuples: if gold_preproc: raw_text = None else: paragraph_tuples = merge_sents(paragraph_tuples) docs, paragraph_tuples = cls._make_docs(nlp, raw_text, paragraph_tuples, gold_preproc, noise_level=noise_level, orth_variant_level=orth_variant_level) golds = cls._make_golds(docs, paragraph_tuples, make_projective, ignore_misaligned=ignore_misaligned) for doc, gold in zip(docs, golds): if gold is not None: if (not max_length) or len(doc) < max_length: yield doc, gold @classmethod def _make_docs(cls, nlp, raw_text, paragraph_tuples, gold_preproc, noise_level=0.0, orth_variant_level=0.0): if raw_text is not None: raw_text, paragraph_tuples = make_orth_variants(nlp, raw_text, paragraph_tuples, orth_variant_level=orth_variant_level) raw_text = add_noise(raw_text, noise_level) return [nlp.make_doc(raw_text)], paragraph_tuples else: docs = [] raw_text, paragraph_tuples = make_orth_variants(nlp, None, paragraph_tuples, orth_variant_level=orth_variant_level) return [Doc(nlp.vocab, words=add_noise(sent_tuples[1], noise_level)) for (sent_tuples, brackets) in paragraph_tuples], paragraph_tuples @classmethod def _make_golds(cls, docs, paragraph_tuples, make_projective, ignore_misaligned=False): if len(docs) != len(paragraph_tuples): n_annots = len(paragraph_tuples) raise ValueError(Errors.E070.format(n_docs=len(docs), n_annots=n_annots)) golds = [] for doc, (sent_tuples, (cats, brackets)) in zip(docs, paragraph_tuples): try: gold = GoldParse.from_annot_tuples(doc, sent_tuples, cats=cats, make_projective=make_projective) except AlignmentError: if ignore_misaligned: gold = None else: raise golds.append(gold) return golds def make_orth_variants(nlp, raw, paragraph_tuples, orth_variant_level=0.0): if random.random() >= orth_variant_level: return raw, paragraph_tuples if random.random() >= 0.5: lower = True if raw is not None: raw = raw.lower() ndsv = nlp.Defaults.single_orth_variants ndpv = nlp.Defaults.paired_orth_variants # modify words in paragraph_tuples variant_paragraph_tuples = [] for sent_tuples, brackets in paragraph_tuples: ids, words, tags, heads, labels, ner = sent_tuples if lower: words = [w.lower() for w in words] # single variants punct_choices = [random.choice(x["variants"]) for x in ndsv] for word_idx in range(len(words)): for punct_idx in range(len(ndsv)): if tags[word_idx] in ndsv[punct_idx]["tags"] \ and words[word_idx] in ndsv[punct_idx]["variants"]: words[word_idx] = punct_choices[punct_idx] # paired variants punct_choices = [random.choice(x["variants"]) for x in ndpv] for word_idx in range(len(words)): for punct_idx in range(len(ndpv)): if tags[word_idx] in ndpv[punct_idx]["tags"] \ and words[word_idx] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]): # backup option: random left vs. right from pair pair_idx = random.choice([0, 1]) # best option: rely on paired POS tags like `` / '' if len(ndpv[punct_idx]["tags"]) == 2: pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx]) # next best option: rely on position in variants # (may not be unambiguous, so order of variants matters) else: for pair in ndpv[punct_idx]["variants"]: if words[word_idx] in pair: pair_idx = pair.index(words[word_idx]) words[word_idx] = punct_choices[punct_idx][pair_idx] variant_paragraph_tuples.append(((ids, words, tags, heads, labels, ner), brackets)) # modify raw to match variant_paragraph_tuples if raw is not None: variants = [] for single_variants in ndsv: variants.extend(single_variants["variants"]) for paired_variants in ndpv: variants.extend(list(itertools.chain.from_iterable(paired_variants["variants"]))) # store variants in reverse length order to be able to prioritize # longer matches (e.g., "---" before "--") variants = sorted(variants, key=lambda x: len(x)) variants.reverse() variant_raw = "" raw_idx = 0 # add initial whitespace while raw_idx < len(raw) and re.match("\s", raw[raw_idx]): variant_raw += raw[raw_idx] raw_idx += 1 for sent_tuples, brackets in variant_paragraph_tuples: ids, words, tags, heads, labels, ner = sent_tuples for word in words: match_found = False # add identical word if word not in variants and raw[raw_idx:].startswith(word): variant_raw += word raw_idx += len(word) match_found = True # add variant word else: for variant in variants: if not match_found and \ raw[raw_idx:].startswith(variant): raw_idx += len(variant) variant_raw += word match_found = True # something went wrong, abort # (add a warning message?) if not match_found: return raw, paragraph_tuples # add following whitespace while raw_idx < len(raw) and re.match("\s", raw[raw_idx]): variant_raw += raw[raw_idx] raw_idx += 1 return variant_raw, variant_paragraph_tuples return raw, variant_paragraph_tuples def add_noise(orig, noise_level): if random.random() >= noise_level: return orig elif type(orig) == list: corrupted = [_corrupt(word, noise_level) for word in orig] corrupted = [w for w in corrupted if w] return corrupted else: return "".join(_corrupt(c, noise_level) for c in orig) def _corrupt(c, noise_level): if random.random() >= noise_level: return c elif c in [".", "'", "!", "?", ","]: return "\n" else: return c.lower() def read_json_object(json_corpus_section): """Take a list of JSON-formatted documents (e.g. from an already loaded training data file) and yield tuples in the GoldParse format. json_corpus_section (list): The data. YIELDS (tuple): The reformatted data. """ for json_doc in json_corpus_section: tuple_doc = json_to_tuple(json_doc) for tuple_paragraph in tuple_doc: yield tuple_paragraph def json_to_tuple(doc): """Convert an item in the JSON-formatted training data to the tuple format used by GoldParse. doc (dict): One entry in the training data. YIELDS (tuple): The reformatted data. """ paragraphs = [] for paragraph in doc["paragraphs"]: sents = [] cats = {} for cat in paragraph.get("cats", {}): cats[cat["label"]] = cat["value"] for sent in paragraph["sentences"]: words = [] ids = [] tags = [] heads = [] labels = [] ner = [] for i, token in enumerate(sent["tokens"]): words.append(token["orth"]) ids.append(i) tags.append(token.get('tag', "-")) heads.append(token.get("head", 0) + i) labels.append(token.get("dep", "")) # Ensure ROOT label is case-insensitive if labels[-1].lower() == "root": labels[-1] = "ROOT" ner.append(token.get("ner", "-")) sents.append([ [ids, words, tags, heads, labels, ner], [cats, sent.get("brackets", [])]]) if sents: yield [paragraph.get("raw", None), sents] def read_json_file(loc, docs_filter=None, limit=None): loc = util.ensure_path(loc) if loc.is_dir(): for filename in loc.iterdir(): yield from read_json_file(loc / filename, limit=limit) else: for doc in _json_iterate(loc): if docs_filter is not None and not docs_filter(doc): continue for json_tuple in json_to_tuple(doc): yield json_tuple def _json_iterate(loc): # We should've made these files jsonl...But since we didn't, parse out # the docs one-by-one to reduce memory usage. # It's okay to read in the whole file -- just don't parse it into JSON. cdef bytes py_raw loc = util.ensure_path(loc) with loc.open("rb") as file_: py_raw = file_.read() cdef long file_length = len(py_raw) if file_length > 2 ** 30: user_warning(Warnings.W027.format(size=file_length)) raw = py_raw cdef int square_depth = 0 cdef int curly_depth = 0 cdef int inside_string = 0 cdef int escape = 0 cdef long start = -1 cdef char c cdef char quote = ord('"') cdef char backslash = ord("\\") cdef char open_square = ord("[") cdef char close_square = ord("]") cdef char open_curly = ord("{") cdef char close_curly = ord("}") for i in range(file_length): c = raw[i] if escape: escape = False continue if c == backslash: escape = True continue if c == quote: inside_string = not inside_string continue if inside_string: continue if c == open_square: square_depth += 1 elif c == close_square: square_depth -= 1 elif c == open_curly: if square_depth == 1 and curly_depth == 0: start = i curly_depth += 1 elif c == close_curly: curly_depth -= 1 if square_depth == 1 and curly_depth == 0: py_str = py_raw[start : i + 1].decode("utf8") try: yield srsly.json_loads(py_str) except Exception: print(py_str) raise start = -1 def iob_to_biluo(tags): out = [] tags = list(tags) while tags: out.extend(_consume_os(tags)) out.extend(_consume_ent(tags)) 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 = ["I-%s" % label for _ in range(1, length - 1)] return [start] + middle + [end] cdef class GoldParse: """Collection for training annotations. DOCS: https://spacy.io/api/goldparse """ @classmethod def from_annot_tuples(cls, doc, annot_tuples, cats=None, make_projective=False): _, words, tags, heads, deps, entities = annot_tuples return cls(doc, words=words, tags=tags, heads=heads, deps=deps, entities=entities, cats=cats, make_projective=make_projective) def __init__(self, doc, annot_tuples=None, words=None, tags=None, morphology=None, heads=None, deps=None, entities=None, make_projective=False, cats=None, links=None, **_): """Create a GoldParse. The fields will not be initialized if len(doc) is zero. doc (Doc): The document the annotations refer to. words (iterable): A sequence of unicode word strings. tags (iterable): A sequence of strings, representing tag annotations. heads (iterable): A sequence of integers, representing syntactic head offsets. deps (iterable): A sequence of strings, representing the syntactic relation types. entities (iterable): A sequence of named entity annotations, either as BILUO tag strings, or as `(start_char, end_char, label)` tuples, representing the entity positions. cats (dict): Labels for text classification. Each key in the dictionary may be a string or an int, or a `(start_char, end_char, label)` tuple, indicating that the label is applied to only part of the document (usually a sentence). Unlike entity annotations, label annotations can overlap, i.e. a single word can be covered by multiple labelled spans. The TextCategorizer component expects true examples of a label to have the value 1.0, and negative examples of a label to have the value 0.0. Labels not in the dictionary are treated as missing - the gradient for those labels will be zero. links (dict): A dict with `(start_char, end_char)` keys, and the values being dicts with kb_id:value entries, representing the external IDs in a knowledge base (KB) mapped to either 1.0 or 0.0, indicating positive and negative examples respectively. RETURNS (GoldParse): The newly constructed object. """ self.mem = Pool() self.loss = 0 self.length = len(doc) self.cats = {} if cats is None else dict(cats) self.links = links # orig_annot is used as an iterator in `nlp.evalate` even if self.length == 0, # so set a empty list to avoid error. # if self.lenght > 0, this is modified latter. self.orig_annot = [] # avoid allocating memory if the doc does not contain any tokens if self.length > 0: if words is None: words = [token.text for token in doc] if tags is None: tags = [None for _ in words] if heads is None: heads = [None for _ in words] if deps is None: deps = [None for _ in words] if morphology is None: morphology = [None for _ in words] if entities is None: entities = ["-" for _ in words] elif len(entities) == 0: entities = ["O" for _ in words] else: # Translate the None values to '-', to make processing easier. # See Issue #2603 entities = [(ent if ent is not None else "-") for ent in entities] if not isinstance(entities[0], basestring): # Assume we have entities specified by character offset. entities = biluo_tags_from_offsets(doc, entities) # These are filled by the tagger/parser/entity recogniser self.c.tags = self.mem.alloc(len(doc), sizeof(int)) self.c.heads = self.mem.alloc(len(doc), sizeof(int)) self.c.labels = self.mem.alloc(len(doc), sizeof(attr_t)) self.c.has_dep = self.mem.alloc(len(doc), sizeof(int)) self.c.sent_start = self.mem.alloc(len(doc), sizeof(int)) self.c.ner = self.mem.alloc(len(doc), sizeof(Transition)) self.words = [None] * len(doc) self.tags = [None] * len(doc) self.heads = [None] * len(doc) self.labels = [None] * len(doc) self.ner = [None] * len(doc) self.morphology = [None] * len(doc) # This needs to be done before we align the words if make_projective and heads is not None and deps is not None: heads, deps = nonproj.projectivize(heads, deps) # Do many-to-one alignment for misaligned tokens. # If we over-segment, we'll have one gold word that covers a sequence # of predicted words # If we under-segment, we'll have one predicted word that covers a # sequence of gold words. # If we "mis-segment", we'll have a sequence of predicted words covering # a sequence of gold words. That's many-to-many -- we don't do that. cost, i2j, j2i, i2j_multi, j2i_multi = align([t.orth_ for t in doc], words) self.cand_to_gold = [(j if j >= 0 else None) for j in i2j] self.gold_to_cand = [(i if i >= 0 else None) for i in j2i] annot_tuples = (range(len(words)), words, tags, heads, deps, entities) self.orig_annot = list(zip(*annot_tuples)) for i, gold_i in enumerate(self.cand_to_gold): if doc[i].text.isspace(): self.words[i] = doc[i].text self.tags[i] = "_SP" self.heads[i] = None self.labels[i] = None self.ner[i] = None self.morphology[i] = set() if gold_i is None: if i in i2j_multi: self.words[i] = words[i2j_multi[i]] self.tags[i] = tags[i2j_multi[i]] self.morphology[i] = morphology[i2j_multi[i]] is_last = i2j_multi[i] != i2j_multi.get(i+1) is_first = i2j_multi[i] != i2j_multi.get(i-1) # Set next word in multi-token span as head, until last if not is_last: self.heads[i] = i+1 self.labels[i] = "subtok" else: head_i = heads[i2j_multi[i]] if head_i: self.heads[i] = self.gold_to_cand[head_i] self.labels[i] = deps[i2j_multi[i]] # Now set NER...This is annoying because if we've split # got an entity word split into two, we need to adjust the # BILUO tags. We can't have BB or LL etc. # Case 1: O -- easy. ner_tag = entities[i2j_multi[i]] if ner_tag == "O": self.ner[i] = "O" # Case 2: U. This has to become a B I* L sequence. elif ner_tag.startswith("U-"): if is_first: self.ner[i] = ner_tag.replace("U-", "B-", 1) elif is_last: self.ner[i] = ner_tag.replace("U-", "L-", 1) else: self.ner[i] = ner_tag.replace("U-", "I-", 1) # Case 3: L. If not last, change to I. elif ner_tag.startswith("L-"): if is_last: self.ner[i] = ner_tag else: self.ner[i] = ner_tag.replace("L-", "I-", 1) # Case 4: I. Stays correct elif ner_tag.startswith("I-"): self.ner[i] = ner_tag else: self.words[i] = words[gold_i] self.tags[i] = tags[gold_i] self.morphology[i] = morphology[gold_i] if heads[gold_i] is None: self.heads[i] = None else: self.heads[i] = self.gold_to_cand[heads[gold_i]] self.labels[i] = deps[gold_i] self.ner[i] = entities[gold_i] # Prevent whitespace that isn't within entities from being tagged as # an entity. for i in range(len(self.ner)): if self.tags[i] == "_SP": prev_ner = self.ner[i-1] if i >= 1 else None next_ner = self.ner[i+1] if (i+1) < len(self.ner) else None if prev_ner == "O" or next_ner == "O": self.ner[i] = "O" cycle = nonproj.contains_cycle(self.heads) if cycle is not None: raise ValueError(Errors.E069.format(cycle=cycle, cycle_tokens=" ".join(["'{}'".format(self.words[tok_id]) for tok_id in cycle]), doc_tokens=" ".join(words[:50]))) def __len__(self): """Get the number of gold-standard tokens. RETURNS (int): The number of gold-standard tokens. """ return self.length @property def is_projective(self): """Whether the provided syntactic annotations form a projective dependency tree. """ return not nonproj.is_nonproj_tree(self.heads) property sent_starts: def __get__(self): return [self.c.sent_start[i] for i in range(self.length)] def __set__(self, sent_starts): for gold_i, is_sent_start in enumerate(sent_starts): i = self.gold_to_cand[gold_i] if i is not None: if is_sent_start in (1, True): self.c.sent_start[i] = 1 elif is_sent_start in (-1, False): self.c.sent_start[i] = -1 else: self.c.sent_start[i] = 0 def docs_to_json(docs, id=0, ner_missing_tag="O"): """Convert a list of Doc objects into the JSON-serializable format used by the spacy train command. docs (iterable / Doc): The Doc object(s) to convert. id (int): Id for the JSON. RETURNS (dict): The data in spaCy's JSON format - each input doc will be treated as a paragraph in the output doc """ if isinstance(docs, Doc): docs = [docs] json_doc = {"id": id, "paragraphs": []} for i, doc in enumerate(docs): json_para = {'raw': doc.text, "sentences": [], "cats": []} for cat, val in doc.cats.items(): json_cat = {"label": cat, "value": val} json_para["cats"].append(json_cat) ent_offsets = [(e.start_char, e.end_char, e.label_) for e in doc.ents] biluo_tags = biluo_tags_from_offsets(doc, ent_offsets, missing=ner_missing_tag) for j, sent in enumerate(doc.sents): json_sent = {"tokens": [], "brackets": []} for token in sent: json_token = {"id": token.i, "orth": token.text} if doc.is_tagged: json_token["tag"] = token.tag_ if doc.is_parsed: json_token["head"] = token.head.i-token.i json_token["dep"] = token.dep_ json_token["ner"] = biluo_tags[token.i] json_sent["tokens"].append(json_token) json_para["sentences"].append(json_sent) json_doc["paragraphs"].append(json_para) return json_doc 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: 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] = "U-%s" % label else: biluo[start_token] = "B-%s" % label for i in range(start_token+1, end_token): biluo[i] = "I-%s" % label biluo[end_token] = "L-%s" % 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 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 is_punct_label(label): return label == "P" or label.lower() == "punct"