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grouping clusters of instances per doc+mention

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svlandeg 2019-05-09 18:11:49 +02:00
parent c6ca8649d7
commit 9d089c0410
1 changed files with 44 additions and 31 deletions
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69
examples/pipeline/wiki_entity_linking/train_el.py
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@ -7,7 +7,7 @@ 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 run_el, training_set_creator, kb_creator
from spacy._ml import SpacyVectors, create_default_optimizer, zero_init from spacy._ml import SpacyVectors, create_default_optimizer, zero_init, cosine
from thinc.api import chain from thinc.api import chain
from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
@ -33,14 +33,12 @@ class EL_Model():
self.article_encoder = self._simple_encoder(width=300) self.article_encoder = self._simple_encoder(width=300)
def train_model(self, training_dir, entity_descr_output, limit=None, to_print=True): 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, instances, pos_entities, neg_entities, doc_by_article = self._get_training_data(training_dir,
entity_descr_output, entity_descr_output,
limit, to_print) limit, to_print)
if to_print: if to_print:
print("Training on", len(gold_vectors), "instances") print("Training on", len(instances), "instance clusters")
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() print()
self.sgd_entity = self.begin_training(self.entity_encoder) self.sgd_entity = self.begin_training(self.entity_encoder)
@ -48,11 +46,20 @@ class EL_Model():
losses = {} losses = {}
for inst, label, entity_descr in zip(instances, gold_vectors, entity_descriptions): for inst_cluster in instances:
article = inst.split(sep="_")[0] pos_ex = pos_entities.get(inst_cluster)
entity_id = inst.split(sep="_")[1] neg_exs = neg_entities.get(inst_cluster, [])
if pos_ex and neg_exs:
article = inst_cluster.split(sep="_")[0]
entity_id = inst_cluster.split(sep="_")[1]
article_doc = doc_by_article[article] article_doc = doc_by_article[article]
self.update(article_doc, entity_descr, label, losses=losses) self.update(article_doc, pos_ex, neg_exs, losses=losses)
# TODO
# elif not pos_ex:
# print("Weird. Couldn't find pos example for", inst_cluster)
# elif not neg_exs:
# print("Weird. Couldn't find neg examples for", inst_cluster)
def _simple_encoder(self, width): def _simple_encoder(self, width):
with Model.define_operators({">>": chain}): with Model.define_operators({">>": chain}):
@ -69,20 +76,27 @@ class EL_Model():
sgd = create_default_optimizer(model.ops) sgd = create_default_optimizer(model.ops)
return sgd return sgd
def update(self, article_doc, entity_descr, label, drop=0., losses=None): def update(self, article_doc, true_entity, false_entities, 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) doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
true_entity_encoding, true_entity_bp = self.entity_encoder.begin_update([true_entity], drop=drop)
# true_similarity = cosine(true_entity_encoding, doc_encoding)
# print("true_similarity", true_similarity)
# for false_entity in false_entities:
# false_entity_encoding, false_entity_bp = self.entity_encoder.begin_update([false_entity], drop=drop)
# false_similarity = cosine(false_entity_encoding, doc_encoding)
# print("false_similarity", false_similarity)
# print("entity/article output dim", len(entity_encoding[0]), len(doc_encoding[0])) # print("entity/article output dim", len(entity_encoding[0]), len(doc_encoding[0]))
mse, diffs = self._calculate_similarity(entity_encoding, doc_encoding) mse, diffs = self._calculate_similarity(true_entity_encoding, doc_encoding)
# print() # print()
# TODO: proper backpropagation taking ranking of elements into account ? # TODO: proper backpropagation taking ranking of elements into account ?
# TODO backpropagation also for negative examples # TODO backpropagation also for negative examples
if label: true_entity_bp(diffs, sgd=self.sgd_entity)
entity_bp(diffs, sgd=self.sgd_entity)
article_bp(diffs, sgd=self.sgd_article) article_bp(diffs, sgd=self.sgd_article)
print(mse) print(mse)
@ -115,7 +129,7 @@ class EL_Model():
raise ValueError("To calculate similarity, both vectors should be of equal length") raise ValueError("To calculate similarity, both vectors should be of equal length")
diffs = (vector2 - vector1) diffs = (vector2 - vector1)
error_sum = (diffs ** 2).sum(axis=1) error_sum = (diffs ** 2).sum()
mean_square_error = error_sum / len(vector1) mean_square_error = error_sum / len(vector1)
return float(mean_square_error), diffs return float(mean_square_error), diffs
@ -130,10 +144,10 @@ class EL_Model():
collect_incorrect=True) collect_incorrect=True)
instances = list() instances = list()
entity_descriptions = list()
local_vectors = list() # TODO: local vectors local_vectors = list() # TODO: local vectors
gold_vectors = list()
doc_by_article = dict() doc_by_article = dict()
pos_entities = dict()
neg_entities = dict()
cnt = 0 cnt = 0
for f in listdir(training_dir): for f in listdir(training_dir):
@ -149,25 +163,24 @@ class EL_Model():
doc = self.nlp(text) doc = self.nlp(text)
doc_by_article[article_id] = doc doc_by_article[article_id] = doc
for mention_pos, entity_pos in correct_entries[article_id].items(): for mention, entity_pos in correct_entries[article_id].items():
descr = id_to_descr.get(entity_pos) descr = id_to_descr.get(entity_pos)
if descr: if descr:
instances.append(article_id + "_" + entity_pos) instances.append(article_id + "_" + mention)
doc = self.nlp(descr) doc_descr = self.nlp(descr)
entity_descriptions.append(doc) pos_entities[article_id + "_" + mention] = doc_descr
gold_vectors.append(True)
for mention_neg, entity_negs in incorrect_entries[article_id].items(): for mention, entity_negs in incorrect_entries[article_id].items():
for entity_neg in entity_negs: for entity_neg in entity_negs:
descr = id_to_descr.get(entity_neg) descr = id_to_descr.get(entity_neg)
if descr: if descr:
instances.append(article_id + "_" + entity_neg) doc_descr = self.nlp(descr)
doc = self.nlp(descr) descr_list = neg_entities.get(article_id + "_" + mention, [])
entity_descriptions.append(doc) descr_list.append(doc_descr)
gold_vectors.append(False) neg_entities[article_id + "_" + mention] = descr_list
if to_print: if to_print:
print() print()
print("Processed", cnt, "dev articles") print("Processed", cnt, "dev articles")
print() print()
return instances, gold_vectors, entity_descriptions, doc_by_article return instances, pos_entities, neg_entities, doc_by_article