mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 01:46:28 +03:00
first stab at model - not functional yet
This commit is contained in:
svlandeg
parent
9f33732b96
commit
c6ca8649d7
|
|
@ -6,53 +6,168 @@ import datetime
|
|||
from os import listdir
|
||||
|
||||
from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
|
||||
from examples.pipeline.wiki_entity_linking import wikidata_processor as wd
|
||||
|
||||
from spacy._ml import SpacyVectors, create_default_optimizer, zero_init
|
||||
|
||||
from thinc.api import chain
|
||||
from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
|
||||
from thinc.api import flatten_add_lengths
|
||||
from thinc.t2v import Pooling, sum_pool, mean_pool
|
||||
from thinc.t2t import ExtractWindow, ParametricAttention
|
||||
from thinc.misc import Residual
|
||||
|
||||
""" TODO: this code needs to be implemented in pipes.pyx"""
|
||||
|
||||
|
||||
def train_model(kb, nlp, training_dir, entity_descr_output, limit=None):
|
||||
run_el._prepare_pipeline(nlp, kb)
|
||||
class EL_Model():
|
||||
|
||||
correct_entries, incorrect_entries = training_set_creator.read_training_entities(training_output=training_dir,
|
||||
collect_correct=True,
|
||||
collect_incorrect=True)
|
||||
labels = ["MATCH", "NOMATCH"]
|
||||
name = "entity_linker"
|
||||
|
||||
entities = kb.get_entity_strings()
|
||||
def __init__(self, kb, nlp):
|
||||
run_el._prepare_pipeline(nlp, kb)
|
||||
self.nlp = nlp
|
||||
self.kb = kb
|
||||
|
||||
id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
|
||||
self.entity_encoder = self._simple_encoder(width=300)
|
||||
self.article_encoder = self._simple_encoder(width=300)
|
||||
|
||||
cnt = 0
|
||||
for f in listdir(training_dir):
|
||||
if not limit or cnt < limit:
|
||||
if not run_el.is_dev(f):
|
||||
article_id = f.replace(".txt", "")
|
||||
if cnt % 500 == 0:
|
||||
print(datetime.datetime.now(), "processed", cnt, "files in the dev dataset")
|
||||
cnt += 1
|
||||
with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
|
||||
text = file.read()
|
||||
print()
|
||||
doc = nlp(text)
|
||||
doc_vector = doc.vector
|
||||
print("FILE", f, len(doc_vector), "D vector")
|
||||
def train_model(self, training_dir, entity_descr_output, limit=None, to_print=True):
|
||||
instances, gold_vectors, entity_descriptions, doc_by_article = self._get_training_data(training_dir,
|
||||
entity_descr_output,
|
||||
limit, to_print)
|
||||
|
||||
if to_print:
|
||||
print("Training on", len(gold_vectors), "instances")
|
||||
print(" - pos:", len([x for x in gold_vectors if x]), "instances")
|
||||
print(" - pos:", len([x for x in gold_vectors if not x]), "instances")
|
||||
print()
|
||||
|
||||
self.sgd_entity = self.begin_training(self.entity_encoder)
|
||||
self.sgd_article = self.begin_training(self.article_encoder)
|
||||
|
||||
losses = {}
|
||||
|
||||
for inst, label, entity_descr in zip(instances, gold_vectors, entity_descriptions):
|
||||
article = inst.split(sep="_")[0]
|
||||
entity_id = inst.split(sep="_")[1]
|
||||
article_doc = doc_by_article[article]
|
||||
self.update(article_doc, entity_descr, label, losses=losses)
|
||||
|
||||
def _simple_encoder(self, width):
|
||||
with Model.define_operators({">>": chain}):
|
||||
encoder = SpacyVectors \
|
||||
>> flatten_add_lengths \
|
||||
>> ParametricAttention(width)\
|
||||
>> Pooling(sum_pool) \
|
||||
>> Residual(zero_init(Maxout(width, width)))
|
||||
|
||||
return encoder
|
||||
|
||||
def begin_training(self, model):
|
||||
# TODO ? link_vectors_to_models(self.vocab)
|
||||
sgd = create_default_optimizer(model.ops)
|
||||
return sgd
|
||||
|
||||
def update(self, article_doc, entity_descr, label, drop=0., losses=None):
|
||||
entity_encoding, entity_bp = self.entity_encoder.begin_update([entity_descr], drop=drop)
|
||||
doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
|
||||
|
||||
# print("entity/article output dim", len(entity_encoding[0]), len(doc_encoding[0]))
|
||||
|
||||
mse, diffs = self._calculate_similarity(entity_encoding, doc_encoding)
|
||||
|
||||
# print()
|
||||
|
||||
# TODO: proper backpropagation taking ranking of elements into account ?
|
||||
# TODO backpropagation also for negative examples
|
||||
if label:
|
||||
entity_bp(diffs, sgd=self.sgd_entity)
|
||||
article_bp(diffs, sgd=self.sgd_article)
|
||||
print(mse)
|
||||
|
||||
|
||||
# TODO delete ?
|
||||
def _simple_cnn_model(self, internal_dim):
|
||||
nr_class = len(self.labels)
|
||||
with Model.define_operators({">>": chain}):
|
||||
model_entity = SpacyVectors >> flatten_add_lengths >> Pooling(mean_pool) # entity encoding
|
||||
model_doc = SpacyVectors >> flatten_add_lengths >> Pooling(mean_pool) # doc encoding
|
||||
output_layer = Softmax(nr_class, internal_dim*2)
|
||||
model = (model_entity | model_doc) >> output_layer
|
||||
# model.tok2vec = chain(tok2vec, flatten)
|
||||
model.nO = nr_class
|
||||
return model
|
||||
|
||||
def predict(self, entity_doc, article_doc):
|
||||
entity_encoding = self.entity_encoder(entity_doc)
|
||||
doc_encoding = self.article_encoder(article_doc)
|
||||
|
||||
print("entity_encodings", len(entity_encoding), entity_encoding)
|
||||
print("doc_encodings", len(doc_encoding), doc_encoding)
|
||||
mse, diffs = self._calculate_similarity(entity_encoding, doc_encoding)
|
||||
print("mse", mse)
|
||||
|
||||
return mse
|
||||
|
||||
def _calculate_similarity(self, vector1, vector2):
|
||||
if len(vector1) != len(vector2):
|
||||
raise ValueError("To calculate similarity, both vectors should be of equal length")
|
||||
|
||||
diffs = (vector2 - vector1)
|
||||
error_sum = (diffs ** 2).sum(axis=1)
|
||||
mean_square_error = error_sum / len(vector1)
|
||||
return float(mean_square_error), diffs
|
||||
|
||||
def _get_labels(self):
|
||||
return tuple(self.labels)
|
||||
|
||||
def _get_training_data(self, training_dir, entity_descr_output, limit, to_print):
|
||||
id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
|
||||
|
||||
correct_entries, incorrect_entries = training_set_creator.read_training_entities(training_output=training_dir,
|
||||
collect_correct=True,
|
||||
collect_incorrect=True)
|
||||
|
||||
instances = list()
|
||||
entity_descriptions = list()
|
||||
local_vectors = list() # TODO: local vectors
|
||||
gold_vectors = list()
|
||||
doc_by_article = dict()
|
||||
|
||||
cnt = 0
|
||||
for f in listdir(training_dir):
|
||||
if not limit or cnt < limit:
|
||||
if not run_el.is_dev(f):
|
||||
article_id = f.replace(".txt", "")
|
||||
if cnt % 500 == 0 and to_print:
|
||||
print(datetime.datetime.now(), "processed", cnt, "files in the dev dataset")
|
||||
cnt += 1
|
||||
if article_id not in doc_by_article:
|
||||
with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
|
||||
text = file.read()
|
||||
doc = self.nlp(text)
|
||||
doc_by_article[article_id] = doc
|
||||
|
||||
for mention_pos, entity_pos in correct_entries[article_id].items():
|
||||
descr = id_to_descr.get(entity_pos)
|
||||
if descr:
|
||||
doc_descr = nlp(descr)
|
||||
descr_vector = doc_descr.vector
|
||||
print("GOLD POS", mention_pos, entity_pos, len(descr_vector), "D vector")
|
||||
instances.append(article_id + "_" + entity_pos)
|
||||
doc = self.nlp(descr)
|
||||
entity_descriptions.append(doc)
|
||||
gold_vectors.append(True)
|
||||
|
||||
for mention_neg, entity_negs in incorrect_entries[article_id].items():
|
||||
for entity_neg in entity_negs:
|
||||
descr = id_to_descr.get(entity_neg)
|
||||
if descr:
|
||||
doc_descr = nlp(descr)
|
||||
descr_vector = doc_descr.vector
|
||||
print("GOLD NEG", mention_neg, entity_neg, len(descr_vector), "D vector")
|
||||
|
||||
print()
|
||||
print("Processed", cnt, "dev articles")
|
||||
print()
|
||||
instances.append(article_id + "_" + entity_neg)
|
||||
doc = self.nlp(descr)
|
||||
entity_descriptions.append(doc)
|
||||
gold_vectors.append(False)
|
||||
|
||||
if to_print:
|
||||
print()
|
||||
print("Processed", cnt, "dev articles")
|
||||
print()
|
||||
return instances, gold_vectors, entity_descriptions, doc_by_article
|
||||
|
|
|
|||
|
|
@ -1,7 +1,8 @@
|
|||
# coding: utf-8
|
||||
from __future__ import unicode_literals
|
||||
|
||||
from examples.pipeline.wiki_entity_linking import wikipedia_processor as wp, kb_creator, training_set_creator, run_el, train_el
|
||||
from examples.pipeline.wiki_entity_linking import wikipedia_processor as wp, kb_creator, training_set_creator, run_el
|
||||
from examples.pipeline.wiki_entity_linking.train_el import EL_Model
|
||||
|
||||
import spacy
|
||||
from spacy.vocab import Vocab
|
||||
|
|
@ -31,17 +32,17 @@ if __name__ == "__main__":
|
|||
# one-time methods to create KB and write to file
|
||||
to_create_prior_probs = False
|
||||
to_create_entity_counts = False
|
||||
to_create_kb = True
|
||||
to_create_kb = False
|
||||
|
||||
# read KB back in from file
|
||||
to_read_kb = True
|
||||
to_test_kb = True
|
||||
to_test_kb = False
|
||||
|
||||
# create training dataset
|
||||
create_wp_training = False
|
||||
|
||||
# run training
|
||||
run_training = False
|
||||
run_training = True
|
||||
|
||||
# apply named entity linking to the dev dataset
|
||||
apply_to_dev = False
|
||||
|
|
@ -105,16 +106,17 @@ if __name__ == "__main__":
|
|||
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)
|
||||
|
||||
# STEP 7: apply the EL algorithm on the training dataset
|
||||
# STEP 6: apply the EL algorithm on the training dataset
|
||||
if run_training:
|
||||
print("STEP 6: training ", datetime.datetime.now())
|
||||
my_nlp = spacy.load('en_core_web_sm')
|
||||
train_el.train_model(kb=my_kb, nlp=my_nlp, training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, limit=5)
|
||||
my_nlp = spacy.load('en_core_web_md')
|
||||
trainer = EL_Model(kb=my_kb, nlp=my_nlp)
|
||||
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, limit=50)
|
||||
print()
|
||||
|
||||
# STEP 8: apply the EL algorithm on the dev dataset
|
||||
# STEP 7: apply the EL algorithm on the dev dataset
|
||||
if apply_to_dev:
|
||||
my_nlp = spacy.load('en_core_web_sm')
|
||||
my_nlp = spacy.load('en_core_web_md')
|
||||
run_el.run_el_dev(kb=my_kb, nlp=my_nlp, training_dir=TRAINING_DIR, limit=2000)
|
||||
print()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user