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Tidy up gold

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ines 2017-10-27 17:02:55 +02:00
parent 6a0483b7aa
commit a6135336f5
1 changed files with 43 additions and 36 deletions
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spacy/gold.pyx
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@ -54,7 +54,8 @@ def merge_sents(sents):
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_brackets.extend((b['first'] + i, b['last'] + i, b['label'])
for b in brackets)
i += len(ids)
return [(m_deps, m_brackets)]
@ -80,6 +81,8 @@ def align(cand_words, gold_words):
punct_re = re.compile(r'\W')
def _min_edit_path(cand_words, gold_words):
cdef:
Pool mem
@ -98,9 +101,9 @@ def _min_edit_path(cand_words, gold_words):
mem = Pool()
n_cand = len(cand_words)
n_gold = len(gold_words)
# Levenshtein distance, except we need the history, and we may want different
# costs.
# Mark operations with a string, and score the history using _edit_cost.
# Levenshtein distance, except we need the history, and we may want
# different costs. Mark operations with a string, and score the history
# using _edit_cost.
previous_row = []
prev_costs = <int*>mem.alloc(n_gold + 1, sizeof(int))
curr_costs = <int*>mem.alloc(n_gold + 1, sizeof(int))
@ -144,9 +147,9 @@ def _min_edit_path(cand_words, gold_words):
def minibatch(items, size=8):
'''Iterate over batches of items. `size` may be an iterator,
"""Iterate over batches of items. `size` may be an iterator,
so that batch-size can vary on each step.
'''
"""
if isinstance(size, int):
size_ = itertools.repeat(8)
else:
@ -168,6 +171,7 @@ class GoldCorpus(object):
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.train_path = util.ensure_path(train_path)
self.dev_path = util.ensure_path(dev_path)
@ -213,7 +217,7 @@ class GoldCorpus(object):
train_tuples = self.train_tuples
if projectivize:
train_tuples = nonproj.preprocess_training_data(
self.train_tuples, label_freq_cutoff=100)
self.train_tuples, label_freq_cutoff=100)
random.shuffle(train_tuples)
gold_docs = self.iter_gold_docs(nlp, train_tuples, gold_preproc,
max_length=max_length,
@ -222,7 +226,6 @@ class GoldCorpus(object):
def dev_docs(self, nlp, gold_preproc=False):
gold_docs = self.iter_gold_docs(nlp, self.dev_tuples, gold_preproc)
#gold_docs = nlp.preprocess_gold(gold_docs)
yield from gold_docs
@classmethod
@ -233,7 +236,6 @@ class GoldCorpus(object):
raw_text = None
else:
paragraph_tuples = merge_sents(paragraph_tuples)
docs = cls._make_docs(nlp, raw_text, paragraph_tuples,
gold_preproc, noise_level=noise_level)
golds = cls._make_golds(docs, paragraph_tuples)
@ -248,17 +250,20 @@ class GoldCorpus(object):
raw_text = add_noise(raw_text, noise_level)
return [nlp.make_doc(raw_text)]
else:
return [Doc(nlp.vocab, words=add_noise(sent_tuples[1], noise_level))
for (sent_tuples, brackets) in paragraph_tuples]
return [Doc(nlp.vocab,
words=add_noise(sent_tuples[1], noise_level))
for (sent_tuples, brackets) in paragraph_tuples]
@classmethod
def _make_golds(cls, docs, paragraph_tuples):
assert len(docs) == len(paragraph_tuples)
if len(docs) == 1:
return [GoldParse.from_annot_tuples(docs[0], paragraph_tuples[0][0])]
return [GoldParse.from_annot_tuples(docs[0],
paragraph_tuples[0][0])]
else:
return [GoldParse.from_annot_tuples(doc, sent_tuples)
for doc, (sent_tuples, brackets) in zip(docs, paragraph_tuples)]
for doc, (sent_tuples, brackets)
in zip(docs, paragraph_tuples)]
@staticmethod
def walk_corpus(path):
@ -330,16 +335,16 @@ def read_json_file(loc, docs_filter=None, limit=None):
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',''))
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],
sent.get('brackets', [])])
sent.get('brackets', [])])
if sents:
yield [paragraph.get('raw', None), sents]
@ -382,19 +387,21 @@ cdef class GoldParse:
@classmethod
def from_annot_tuples(cls, doc, annot_tuples, make_projective=False):
_, words, tags, heads, deps, entities = annot_tuples
return cls(doc, words=words, tags=tags, heads=heads, deps=deps, entities=entities,
make_projective=make_projective)
return cls(doc, words=words, tags=tags, heads=heads, deps=deps,
entities=entities, make_projective=make_projective)
def __init__(self, doc, annot_tuples=None, words=None, tags=None, heads=None,
deps=None, entities=None, make_projective=False,
def __init__(self, doc, annot_tuples=None, words=None, tags=None,
heads=None, deps=None, entities=None, make_projective=False,
cats=None):
"""Create a GoldParse.
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.
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.
@ -404,9 +411,10 @@ cdef class GoldParse:
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.
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.
RETURNS (GoldParse): The newly constructed object.
"""
if words is None:
@ -470,11 +478,11 @@ cdef class GoldParse:
self.ner[i] = entities[gold_i]
cycle = nonproj.contains_cycle(self.heads)
if cycle != None:
if cycle is not None:
raise Exception("Cycle found: %s" % cycle)
if make_projective:
proj_heads,_ = nonproj.projectivize(self.heads, self.labels)
proj_heads, _ = nonproj.projectivize(self.heads, self.labels)
self.heads = proj_heads
def __len__(self):
@ -497,20 +505,19 @@ cdef class GoldParse:
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).
"""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.
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
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.