mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-30 23:47:31 +03:00
update per entity
This commit is contained in:
svlandeg
parent
eb08bdb11f
commit
1a16490d20
|
|
@ -154,7 +154,7 @@ class EL_Model:
|
|||
if self.PRINT_F:
|
||||
print("p/r/F", print_string, round(p, 1), round(r, 1), round(f, 1))
|
||||
|
||||
loss, d_scores = self.get_loss(self.model.ops.asarray(predictions), self.model.ops.asarray(golds))
|
||||
loss, gradient = self.get_loss(self.model.ops.asarray(predictions), self.model.ops.asarray(golds))
|
||||
if self.PRINT_LOSS:
|
||||
print("loss", print_string, round(loss, 5))
|
||||
|
||||
|
|
@ -235,62 +235,58 @@ class EL_Model:
|
|||
@staticmethod
|
||||
def get_loss(predictions, golds):
|
||||
d_scores = (predictions - golds)
|
||||
|
||||
loss = (d_scores ** 2).sum()
|
||||
loss = (d_scores ** 2).mean()
|
||||
return loss, d_scores
|
||||
|
||||
# TODO: multiple docs/articles
|
||||
def update(self, article_text, entities, golds, apply_threshold=True):
|
||||
article_doc = self.nlp(article_text)
|
||||
doc_encodings, bp_doc = self.article_encoder.begin_update([article_doc], drop=self.DROP)
|
||||
doc_encoding = doc_encodings[0]
|
||||
# entity_docs = list(self.nlp.pipe(entities))
|
||||
|
||||
entity_docs = list(self.nlp.pipe(entities))
|
||||
# print("entity_docs", type(entity_docs))
|
||||
for entity, gold in zip(entities, golds):
|
||||
doc_encodings, bp_doc = self.article_encoder.begin_update([article_doc], drop=self.DROP)
|
||||
doc_encoding = doc_encodings[0]
|
||||
|
||||
entity_encodings, bp_entity = self.entity_encoder.begin_update(entity_docs, drop=self.DROP)
|
||||
# print("entity_encodings", len(entity_encodings), entity_encodings)
|
||||
entity_doc = self.nlp(entity)
|
||||
# print("entity_docs", type(entity_doc))
|
||||
|
||||
concat_encodings = [list(entity_encodings[i]) + list(doc_encoding) for i in range(len(entities))]
|
||||
# print("concat_encodings", len(concat_encodings), concat_encodings)
|
||||
entity_encodings, bp_entity = self.entity_encoder.begin_update([entity_doc], drop=self.DROP)
|
||||
entity_encoding = entity_encodings[0]
|
||||
# print("entity_encoding", len(entity_encoding), entity_encoding)
|
||||
|
||||
predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP)
|
||||
predictions = self.model.ops.flatten(predictions)
|
||||
concat_encodings = [list(entity_encoding) + list(doc_encoding)] # for i in range(len(entities))
|
||||
# print("concat_encodings", len(concat_encodings), concat_encodings)
|
||||
|
||||
# print("predictions", predictions)
|
||||
golds = self.model.ops.asarray(golds)
|
||||
# print("golds", golds)
|
||||
prediction, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP)
|
||||
# predictions = self.model.ops.flatten(predictions)
|
||||
|
||||
loss, d_scores = self.get_loss(predictions, golds)
|
||||
# print("prediction", prediction)
|
||||
# golds = self.model.ops.asarray(golds)
|
||||
# print("gold", gold)
|
||||
|
||||
if self.PRINT_LOSS and self.PRINT_TRAIN:
|
||||
print("loss train", round(loss, 5))
|
||||
loss, gradient = self.get_loss(prediction, gold)
|
||||
|
||||
if self.PRINT_F and self.PRINT_TRAIN:
|
||||
predictions_f = [x for x in predictions]
|
||||
if apply_threshold:
|
||||
predictions_f = [float(1.0) if x > self.CUTOFF else float(0.0) for x in predictions_f]
|
||||
p, r, f = run_el.evaluate(predictions_f, golds, to_print=False)
|
||||
print("p/r/F train", round(p, 1), round(r, 1), round(f, 1))
|
||||
if self.PRINT_LOSS and self.PRINT_TRAIN:
|
||||
print("loss train", round(loss, 5))
|
||||
|
||||
d_scores = d_scores.reshape((-1, 1))
|
||||
d_scores = d_scores.astype(np.float32)
|
||||
# print("d_scores", d_scores)
|
||||
gradient = float(gradient)
|
||||
# print("gradient", gradient)
|
||||
# print("loss", loss)
|
||||
|
||||
model_gradient = bp_model(d_scores, sgd=self.sgd)
|
||||
# print("model_gradient", model_gradient)
|
||||
model_gradient = bp_model(gradient, sgd=self.sgd)
|
||||
# print("model_gradient", model_gradient)
|
||||
|
||||
# concat = entity + doc, but doc is the same within this function (TODO: multiple docs/articles)
|
||||
doc_gradient = model_gradient[0][self.ENTITY_WIDTH:]
|
||||
entity_gradients = list()
|
||||
for x in model_gradient:
|
||||
entity_gradients.append(list(x[0:self.ENTITY_WIDTH]))
|
||||
# concat = entity + doc, but doc is the same within this function (TODO: multiple docs/articles)
|
||||
doc_gradient = model_gradient[0][self.ENTITY_WIDTH:]
|
||||
entity_gradients = list()
|
||||
for x in model_gradient:
|
||||
entity_gradients.append(list(x[0:self.ENTITY_WIDTH]))
|
||||
|
||||
# print("doc_gradient", doc_gradient)
|
||||
# print("entity_gradients", entity_gradients)
|
||||
# print("doc_gradient", doc_gradient)
|
||||
# print("entity_gradients", entity_gradients)
|
||||
|
||||
bp_doc([doc_gradient], sgd=self.sgd_article)
|
||||
bp_entity(entity_gradients, sgd=self.sgd_entity)
|
||||
bp_doc([doc_gradient], sgd=self.sgd_article)
|
||||
bp_entity(entity_gradients, sgd=self.sgd_entity)
|
||||
|
||||
def _get_training_data(self, training_dir, entity_descr_output, dev, limit, balance, to_print):
|
||||
id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
|
||||
|
|
@ -326,16 +322,17 @@ class EL_Model:
|
|||
pos_entities[article_id + "_" + mention] = descr
|
||||
|
||||
for mention, entity_negs in incorrect_entries[article_id].items():
|
||||
neg_count = 0
|
||||
for entity_neg in entity_negs:
|
||||
descr = id_to_descr.get(entity_neg)
|
||||
if descr:
|
||||
if not balance or pos_entities.get(article_id + "_" + mention):
|
||||
neg_count = 0
|
||||
for entity_neg in entity_negs:
|
||||
# if balance, keep only 1 negative instance for each positive instance
|
||||
if neg_count < 1 or not balance:
|
||||
descr_list = neg_entities.get(article_id + "_" + mention, [])
|
||||
descr_list.append(descr)
|
||||
neg_entities[article_id + "_" + mention] = descr_list
|
||||
neg_count += 1
|
||||
descr = id_to_descr.get(entity_neg)
|
||||
if descr:
|
||||
descr_list = neg_entities.get(article_id + "_" + mention, [])
|
||||
descr_list.append(descr)
|
||||
neg_entities[article_id + "_" + mention] = descr_list
|
||||
neg_count += 1
|
||||
|
||||
if to_print:
|
||||
print()
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ if __name__ == "__main__":
|
|||
print("STEP 6: training", datetime.datetime.now())
|
||||
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, trainlimit=1000, devlimit=20)
|
||||
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=100, devlimit=20)
|
||||
print()
|
||||
|
||||
# STEP 7: apply the EL algorithm on the dev dataset
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user