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write entity linking pipe to file and keep vocab consistent between kb and nlp

This commit is contained in:
svlandeg 2019-06-13 16:25:39 +02:00
parent b12001f368
commit 78dd3e11da
5 changed files with 226 additions and 93 deletions
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4
examples/pipeline/wiki_entity_linking/kb_creator.py
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@ -40,8 +40,8 @@ def create_kb(nlp, max_entities_per_alias, min_occ,
title_list = list(title_to_id.keys())
# TODO: remove this filter (just for quicker testing of code)
# title_list = title_list[0:34200]
# title_to_id = {t: title_to_id[t] for t in title_list}
title_list = title_list[0:342]
title_to_id = {t: title_to_id[t] for t in title_list}
entity_list = [title_to_id[x] for x in title_list]

145
examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
View File

@ -6,6 +6,7 @@ import random
from spacy.util import minibatch, compounding
from examples.pipeline.wiki_entity_linking import wikipedia_processor as wp, kb_creator, training_set_creator, run_el
from examples.pipeline.wiki_entity_linking.kb_creator import DESC_WIDTH
import spacy
from spacy.vocab import Vocab
@ -22,41 +23,48 @@ ENTITY_DEFS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_defs.csv'
ENTITY_DESCR = 'C:/Users/Sofie/Documents/data/wikipedia/entity_descriptions.csv'
KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb'
VOCAB_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/vocab'
NLP_1_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_1'
NLP_2_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_2'
TRAINING_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/training_data_nel/'
MAX_CANDIDATES = 10
MIN_PAIR_OCC = 5
DOC_CHAR_CUTOFF = 300
EPOCHS = 10
EPOCHS = 2
DROPOUT = 0.1
def run_pipeline():
print("START", datetime.datetime.now())
print()
nlp = spacy.load('en_core_web_lg')
my_kb = None
nlp_1 = spacy.load('en_core_web_lg')
nlp_2 = None
kb_1 = None
kb_2 = None
# one-time methods to create KB and write to file
to_create_prior_probs = False
to_create_entity_counts = False
to_create_kb = False
to_create_kb = True
# read KB back in from file
to_read_kb = True
to_test_kb = False
to_test_kb = True
# create training dataset
create_wp_training = False
# train the EL pipe
train_pipe = True
measure_performance = False
# test the EL pipe on a simple example
to_test_pipeline = True
# write the NLP object, read back in and test again
test_nlp_io = True
# STEP 1 : create prior probabilities from WP
# run only once !
if to_create_prior_probs:
@ -75,7 +83,7 @@ def run_pipeline():
# run only once !
if to_create_kb:
print("STEP 3a: to_create_kb", datetime.datetime.now())
my_kb = kb_creator.create_kb(nlp,
kb_1 = kb_creator.create_kb(nlp_1,
max_entities_per_alias=MAX_CANDIDATES,
min_occ=MIN_PAIR_OCC,
entity_def_output=ENTITY_DEFS,
@ -83,63 +91,66 @@ def run_pipeline():
count_input=ENTITY_COUNTS,
prior_prob_input=PRIOR_PROB,
to_print=False)
print("kb entities:", my_kb.get_size_entities())
print("kb aliases:", my_kb.get_size_aliases())
print("kb entities:", kb_1.get_size_entities())
print("kb aliases:", kb_1.get_size_aliases())
print()
print("STEP 3b: write KB", datetime.datetime.now())
my_kb.dump(KB_FILE)
nlp.vocab.to_disk(VOCAB_DIR)
print("STEP 3b: write KB and NLP", datetime.datetime.now())
kb_1.dump(KB_FILE)
nlp_1.to_disk(NLP_1_DIR)
print()
# STEP 4 : read KB back in from file
if to_read_kb:
print("STEP 4: to_read_kb", datetime.datetime.now())
my_vocab = Vocab()
my_vocab.from_disk(VOCAB_DIR)
my_kb = KnowledgeBase(vocab=my_vocab, entity_vector_length=64) # TODO entity vectors
my_kb.load_bulk(KB_FILE)
print("kb entities:", my_kb.get_size_entities())
print("kb aliases:", my_kb.get_size_aliases())
# my_vocab = Vocab()
# my_vocab.from_disk(VOCAB_DIR)
# my_kb = KnowledgeBase(vocab=my_vocab, entity_vector_length=64)
nlp_2 = spacy.load(NLP_1_DIR)
kb_2 = KnowledgeBase(vocab=nlp_2.vocab, entity_vector_length=DESC_WIDTH)
kb_2.load_bulk(KB_FILE)
print("kb entities:", kb_2.get_size_entities())
print("kb aliases:", kb_2.get_size_aliases())
print()
# test KB
if to_test_kb:
run_el.run_kb_toy_example(kb=my_kb)
run_el.run_kb_toy_example(kb=kb_2)
print()
# STEP 5: create a training dataset from WP
if create_wp_training:
print("STEP 5: create training dataset", datetime.datetime.now())
training_set_creator.create_training(kb=my_kb, entity_def_input=ENTITY_DEFS, training_output=TRAINING_DIR)
training_set_creator.create_training(kb=kb_2, entity_def_input=ENTITY_DEFS, training_output=TRAINING_DIR)
# STEP 6: create the entity linking pipe
if train_pipe:
print("STEP 6: training Entity Linking pipe", datetime.datetime.now())
train_limit = 5000
dev_limit = 1000
train_limit = 10
dev_limit = 5
print("Training on", train_limit, "articles")
print("Dev testing on", dev_limit, "articles")
print()
train_data = training_set_creator.read_training(nlp=nlp,
train_data = training_set_creator.read_training(nlp=nlp_2,
training_dir=TRAINING_DIR,
dev=False,
limit=train_limit,
to_print=False)
dev_data = training_set_creator.read_training(nlp=nlp,
dev_data = training_set_creator.read_training(nlp=nlp_2,
training_dir=TRAINING_DIR,
dev=True,
limit=dev_limit,
to_print=False)
el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": my_kb, "doc_cutoff": DOC_CHAR_CUTOFF})
nlp.add_pipe(el_pipe, last=True)
el_pipe = nlp_2.create_pipe(name='entity_linker', config={"doc_cutoff": DOC_CHAR_CUTOFF})
el_pipe.set_kb(kb_2)
nlp_2.add_pipe(el_pipe, last=True)
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "entity_linker"]
with nlp.disable_pipes(*other_pipes): # only train Entity Linking
nlp.begin_training()
other_pipes = [pipe for pipe in nlp_2.pipe_names if pipe != "entity_linker"]
with nlp_2.disable_pipes(*other_pipes): # only train Entity Linking
nlp_2.begin_training()
for itn in range(EPOCHS):
random.shuffle(train_data)
@ -147,11 +158,11 @@ def run_pipeline():
batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
batchnr = 0
with nlp.disable_pipes(*other_pipes):
with nlp_2.disable_pipes(*other_pipes):
for batch in batches:
try:
docs, golds = zip(*batch)
nlp.update(
nlp_2.update(
docs,
golds,
drop=DROPOUT,
@ -164,40 +175,62 @@ def run_pipeline():
losses['entity_linker'] = losses['entity_linker'] / batchnr
print("Epoch, train loss", itn, round(losses['entity_linker'], 2))
print()
print("STEP 7: performance measurement of Entity Linking pipe", datetime.datetime.now())
print()
if measure_performance:
print()
print("STEP 7: performance measurement of Entity Linking pipe", datetime.datetime.now())
print()
# print(" measuring accuracy 1-1")
el_pipe.context_weight = 1
el_pipe.prior_weight = 1
dev_acc_1_1 = _measure_accuracy(dev_data, el_pipe)
train_acc_1_1 = _measure_accuracy(train_data, el_pipe)
print("train/dev acc combo:", round(train_acc_1_1, 2), round(dev_acc_1_1, 2))
# print(" measuring accuracy 1-1")
el_pipe.context_weight = 1
el_pipe.prior_weight = 1
dev_acc_1_1 = _measure_accuracy(dev_data, el_pipe)
train_acc_1_1 = _measure_accuracy(train_data, el_pipe)
print("train/dev acc combo:", round(train_acc_1_1, 2), round(dev_acc_1_1, 2))
# baseline using only prior probabilities
el_pipe.context_weight = 0
el_pipe.prior_weight = 1
dev_acc_0_1 = _measure_accuracy(dev_data, el_pipe)
train_acc_0_1 = _measure_accuracy(train_data, el_pipe)
print("train/dev acc prior:", round(train_acc_0_1, 2), round(dev_acc_0_1, 2))
# baseline using only prior probabilities
el_pipe.context_weight = 0
el_pipe.prior_weight = 1
dev_acc_0_1 = _measure_accuracy(dev_data, el_pipe)
train_acc_0_1 = _measure_accuracy(train_data, el_pipe)
print("train/dev acc prior:", round(train_acc_0_1, 2), round(dev_acc_0_1, 2))
# using only context
el_pipe.context_weight = 1
el_pipe.prior_weight = 0
dev_acc_1_0 = _measure_accuracy(dev_data, el_pipe)
train_acc_1_0 = _measure_accuracy(train_data, el_pipe)
# using only context
el_pipe.context_weight = 1
el_pipe.prior_weight = 0
dev_acc_1_0 = _measure_accuracy(dev_data, el_pipe)
train_acc_1_0 = _measure_accuracy(train_data, el_pipe)
print("train/dev acc context:", round(train_acc_1_0, 2), round(dev_acc_1_0, 2))
print()
print("train/dev acc context:", round(train_acc_1_0, 2), round(dev_acc_1_0, 2))
print()
if to_test_pipeline:
print()
print("STEP 8: applying Entity Linking to toy example", datetime.datetime.now())
print()
run_el_toy_example(kb=my_kb, nlp=nlp)
run_el_toy_example(nlp=nlp_2)
print()
if test_nlp_io:
print()
print("STEP 9: testing NLP IO", datetime.datetime.now())
print()
print("writing to", NLP_2_DIR)
print(" vocab len nlp_2", len(nlp_2.vocab))
print(" vocab len kb_2", len(kb_2.vocab))
nlp_2.to_disk(NLP_2_DIR)
print()
print("reading from", NLP_2_DIR)
nlp_3 = spacy.load(NLP_2_DIR)
print(" vocab len nlp_3", len(nlp_3.vocab))
for pipe_name, pipe in nlp_3.pipeline:
if pipe_name == "entity_linker":
print(" vocab len kb_3", len(pipe.kb.vocab))
print()
print("running toy example with NLP 2")
run_el_toy_example(nlp=nlp_3)
print()
print("STOP", datetime.datetime.now())
@ -239,7 +272,7 @@ def _measure_accuracy(data, el_pipe):
return acc
def run_el_toy_example(nlp, kb):
def run_el_toy_example(nlp):
text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
"Douglas reminds us to always bring our towel. " \
"The main character in Doug's novel is the man Arthur Dent, " \
@ -261,4 +294,4 @@ def run_el_toy_example(nlp, kb):
if __name__ == "__main__":
run_pipeline()
run_pipeline()

6
spacy/kb.pyx
View File

@ -2,6 +2,8 @@
# cython: profile=True
# coding: utf8
from collections import OrderedDict
from pathlib import Path, WindowsPath
from cpython.exc cimport PyErr_CheckSignals
from spacy import util
@ -389,6 +391,8 @@ cdef class Writer:
def __init__(self, object loc):
if path.exists(loc):
assert not path.isdir(loc), "%s is directory." % loc
if isinstance(loc, Path):
loc = bytes(loc)
cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
self._fp = fopen(<char*>bytes_loc, 'wb')
assert self._fp != NULL
@ -431,6 +435,8 @@ cdef class Reader:
def __init__(self, object loc):
assert path.exists(loc)
assert not path.isdir(loc)
if isinstance(loc, Path):
loc = bytes(loc)
cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
self._fp = fopen(<char*>bytes_loc, 'rb')
if not self._fp:

9
spacy/language.py
View File

@ -11,6 +11,7 @@ from copy import copy, deepcopy
from thinc.neural import Model
import srsly
from spacy.kb import KnowledgeBase
from .tokenizer import Tokenizer
from .vocab import Vocab
from .lemmatizer import Lemmatizer
@ -809,6 +810,14 @@ class Language(object):
# Convert to list here in case exclude is (default) tuple
exclude = list(exclude) + ["vocab"]
util.from_disk(path, deserializers, exclude)
# download the KB for the entity linking component - requires the vocab
for pipe_name, pipe in self.pipeline:
if pipe_name == "entity_linker":
kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=pipe.cfg["entity_width"])
kb.load_bulk(path / pipe_name / "kb")
pipe.set_kb(kb)
self._path = path
return self

155
spacy/pipeline/pipes.pyx
View File

@ -14,6 +14,7 @@ from thinc.misc import LayerNorm
from thinc.neural.util import to_categorical
from thinc.neural.util import get_array_module
from spacy.kb import KnowledgeBase
from ..tokens.doc cimport Doc
from ..syntax.nn_parser cimport Parser
from ..syntax.ner cimport BiluoPushDown
@ -1077,7 +1078,7 @@ class EntityLinker(Pipe):
hidden_width = cfg.get("hidden_width", 32)
article_width = cfg.get("article_width", 128)
sent_width = cfg.get("sent_width", 64)
entity_width = cfg["kb"].entity_vector_length
entity_width = cfg.get("entity_width") # no default because this needs to correspond with the KB
article_encoder = build_nel_encoder(in_width=embed_width, hidden_width=hidden_width, end_width=article_width, **cfg)
sent_encoder = build_nel_encoder(in_width=embed_width, hidden_width=hidden_width, end_width=sent_width, **cfg)
@ -1089,34 +1090,41 @@ class EntityLinker(Pipe):
return article_encoder, sent_encoder, mention_encoder
def __init__(self, **cfg):
self.article_encoder = True
self.sent_encoder = True
self.mention_encoder = True
self.kb = None
self.cfg = dict(cfg)
self.kb = self.cfg["kb"]
self.doc_cutoff = self.cfg["doc_cutoff"]
def use_avg_params(self):
# Modify the pipe's encoders/models, to use their average parameter values.
# TODO: this doesn't work yet because there's no exit method
self.article_encoder.use_params(self.sgd_article.averages)
self.sent_encoder.use_params(self.sgd_sent.averages)
self.mention_encoder.use_params(self.sgd_mention.averages)
self.doc_cutoff = self.cfg.get("doc_cutoff", 150)
def set_kb(self, kb):
self.kb = kb
def require_model(self):
# Raise an error if the component's model is not initialized.
if getattr(self, "mention_encoder", None) in (None, True, False):
raise ValueError(Errors.E109.format(name=self.name))
def require_kb(self):
# Raise an error if the knowledge base is not initialized.
if getattr(self, "kb", None) in (None, True, False):
# TODO: custom error
raise ValueError(Errors.E109.format(name=self.name))
def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
self.require_kb()
self.cfg["entity_width"] = self.kb.entity_vector_length
if self.mention_encoder is True:
self.article_encoder, self.sent_encoder, self.mention_encoder = self.Model(**self.cfg)
self.sgd_article = create_default_optimizer(self.article_encoder.ops)
self.sgd_sent = create_default_optimizer(self.sent_encoder.ops)
self.sgd_mention = create_default_optimizer(self.mention_encoder.ops)
self.sgd_article = create_default_optimizer(self.article_encoder.ops)
self.sgd_sent = create_default_optimizer(self.sent_encoder.ops)
self.sgd_mention = create_default_optimizer(self.mention_encoder.ops)
return self.sgd_article
def update(self, docs, golds, state=None, drop=0.0, sgd=None, losses=None):
self.require_model()
self.require_kb()
if len(docs) != len(golds):
raise ValueError(Errors.E077.format(value="EL training", n_docs=len(docs),
@ -1220,6 +1228,7 @@ class EntityLinker(Pipe):
def predict(self, docs):
self.require_model()
self.require_kb()
if isinstance(docs, Doc):
docs = [docs]
@ -1228,30 +1237,32 @@ class EntityLinker(Pipe):
final_kb_ids = list()
for i, article_doc in enumerate(docs):
doc_encoding = self.article_encoder([article_doc])
for ent in article_doc.ents:
sent_doc = ent.sent.as_doc()
sent_encoding = self.sent_encoder([sent_doc])
concat_encoding = [list(doc_encoding[0]) + list(sent_encoding[0])]
mention_encoding = self.mention_encoder(np.asarray([concat_encoding[0]]))
mention_enc_t = np.transpose(mention_encoding)
if len(article_doc) > 0:
doc_encoding = self.article_encoder([article_doc])
for ent in article_doc.ents:
sent_doc = ent.sent.as_doc()
if len(sent_doc) > 0:
sent_encoding = self.sent_encoder([sent_doc])
concat_encoding = [list(doc_encoding[0]) + list(sent_encoding[0])]
mention_encoding = self.mention_encoder(np.asarray([concat_encoding[0]]))
mention_enc_t = np.transpose(mention_encoding)
candidates = self.kb.get_candidates(ent.text)
if candidates:
scores = list()
for c in candidates:
prior_prob = c.prior_prob * self.prior_weight
kb_id = c.entity_
entity_encoding = c.entity_vector
sim = cosine(np.asarray([entity_encoding]), mention_enc_t) * self.context_weight
score = prior_prob + sim - (prior_prob*sim) # put weights on the different factors ?
scores.append(score)
candidates = self.kb.get_candidates(ent.text)
if candidates:
scores = list()
for c in candidates:
prior_prob = c.prior_prob * self.prior_weight
kb_id = c.entity_
entity_encoding = c.entity_vector
sim = cosine(np.asarray([entity_encoding]), mention_enc_t) * self.context_weight
score = prior_prob + sim - (prior_prob*sim) # put weights on the different factors ?
scores.append(score)
# TODO: thresholding
best_index = scores.index(max(scores))
best_candidate = candidates[best_index]
final_entities.append(ent)
final_kb_ids.append(best_candidate.entity_)
# TODO: thresholding
best_index = scores.index(max(scores))
best_candidate = candidates[best_index]
final_entities.append(ent)
final_kb_ids.append(best_candidate.entity_)
return final_entities, final_kb_ids
@ -1260,6 +1271,80 @@ class EntityLinker(Pipe):
for token in entity:
token.ent_kb_id_ = kb_id
def to_bytes(self, exclude=tuple(), **kwargs):
"""Serialize the pipe to a bytestring.
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
"""
serialize = OrderedDict()
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
serialize["kb"] = self.kb.to_bytes # TODO
if self.mention_encoder not in (True, False, None):
serialize["article_encoder"] = self.article_encoder.to_bytes
serialize["sent_encoder"] = self.sent_encoder.to_bytes
serialize["mention_encoder"] = self.mention_encoder.to_bytes
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
"""Load the pipe from a bytestring."""
deserialize = OrderedDict()
deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b))
deserialize["kb"] = lambda b: self.kb.from_bytes(b) # TODO
deserialize["article_encoder"] = lambda b: self.article_encoder.from_bytes(b)
deserialize["sent_encoder"] = lambda b: self.sent_encoder.from_bytes(b)
deserialize["mention_encoder"] = lambda b: self.mention_encoder.from_bytes(b)
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, exclude=tuple(), **kwargs):
"""Serialize the pipe to disk."""
serialize = OrderedDict()
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
serialize["kb"] = lambda p: self.kb.dump(p)
if self.mention_encoder not in (None, True, False):
serialize["article_encoder"] = lambda p: p.open("wb").write(self.article_encoder.to_bytes())
serialize["sent_encoder"] = lambda p: p.open("wb").write(self.sent_encoder.to_bytes())
serialize["mention_encoder"] = lambda p: p.open("wb").write(self.mention_encoder.to_bytes())
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude=tuple(), **kwargs):
"""Load the pipe from disk."""
def load_article_encoder(p):
if self.article_encoder is True:
self.article_encoder, _, _ = self.Model(**self.cfg)
self.article_encoder.from_bytes(p.open("rb").read())
def load_sent_encoder(p):
if self.sent_encoder is True:
_, self.sent_encoder, _ = self.Model(**self.cfg)
self.sent_encoder.from_bytes(p.open("rb").read())
def load_mention_encoder(p):
if self.mention_encoder is True:
_, _, self.mention_encoder = self.Model(**self.cfg)
self.mention_encoder.from_bytes(p.open("rb").read())
deserialize = OrderedDict()
deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
deserialize["article_encoder"] = load_article_encoder
deserialize["sent_encoder"] = load_sent_encoder
deserialize["mention_encoder"] = load_mention_encoder
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_disk(path, deserialize, exclude)
return self
def rehearse(self, docs, sgd=None, losses=None, **config):
# TODO
pass
def add_label(self, label):
pass
class Sentencizer(object):
"""Segment the Doc into sentences using a rule-based strategy.