explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-03-13 07:55:49 +03:00
Code Issues Packages Projects Releases Wiki Activity

update per entity

Browse Source
This commit is contained in:
svlandeg 2019-05-22 12:46:40 +02:00
parent eb08bdb11f
commit 1a16490d20
2 changed files with 45 additions and 48 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

91
examples/pipeline/wiki_entity_linking/train_el.py
View File

@ -154,7 +154,7 @@ class EL_Model:
if self.PRINT_F: if self.PRINT_F:
print("p/r/F", print_string, round(p, 1), round(r, 1), round(f, 1)) 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: if self.PRINT_LOSS:
print("loss", print_string, round(loss, 5)) print("loss", print_string, round(loss, 5))
@ -235,62 +235,58 @@ class EL_Model:
@staticmethod @staticmethod
def get_loss(predictions, golds): def get_loss(predictions, golds):
d_scores = (predictions - golds) d_scores = (predictions - golds)
loss = (d_scores ** 2).mean()
loss = (d_scores ** 2).sum()
return loss, d_scores return loss, d_scores
# TODO: multiple docs/articles # TODO: multiple docs/articles
def update(self, article_text, entities, golds, apply_threshold=True): def update(self, article_text, entities, golds, apply_threshold=True):
article_doc = self.nlp(article_text) article_doc = self.nlp(article_text)
doc_encodings, bp_doc = self.article_encoder.begin_update([article_doc], drop=self.DROP) # entity_docs = list(self.nlp.pipe(entities))
doc_encoding = doc_encodings[0]
entity_docs = list(self.nlp.pipe(entities)) for entity, gold in zip(entities, golds):
# print("entity_docs", type(entity_docs)) 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) entity_doc = self.nlp(entity)
# print("entity_encodings", len(entity_encodings), entity_encodings) # print("entity_docs", type(entity_doc))
concat_encodings = [list(entity_encodings[i]) + list(doc_encoding) for i in range(len(entities))] entity_encodings, bp_entity = self.entity_encoder.begin_update([entity_doc], drop=self.DROP)
# print("concat_encodings", len(concat_encodings), concat_encodings) 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) concat_encodings = [list(entity_encoding) + list(doc_encoding)] # for i in range(len(entities))
predictions = self.model.ops.flatten(predictions) # print("concat_encodings", len(concat_encodings), concat_encodings)
# print("predictions", predictions) prediction, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP)
golds = self.model.ops.asarray(golds) # predictions = self.model.ops.flatten(predictions)
# print("golds", golds)
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: loss, gradient = self.get_loss(prediction, gold)
print("loss train", round(loss, 5))
if self.PRINT_F and self.PRINT_TRAIN: if self.PRINT_LOSS and self.PRINT_TRAIN:
predictions_f = [x for x in predictions] print("loss train", round(loss, 5))
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))
d_scores = d_scores.reshape((-1, 1)) gradient = float(gradient)
d_scores = d_scores.astype(np.float32) # print("gradient", gradient)
# print("d_scores", d_scores) # print("loss", loss)
model_gradient = bp_model(d_scores, sgd=self.sgd) model_gradient = bp_model(gradient, sgd=self.sgd)
# print("model_gradient", model_gradient) # print("model_gradient", model_gradient)
# concat = entity + doc, but doc is the same within this function (TODO: multiple docs/articles) # concat = entity + doc, but doc is the same within this function (TODO: multiple docs/articles)
doc_gradient = model_gradient[0][self.ENTITY_WIDTH:] doc_gradient = model_gradient[0][self.ENTITY_WIDTH:]
entity_gradients = list() entity_gradients = list()
for x in model_gradient: for x in model_gradient:
entity_gradients.append(list(x[0:self.ENTITY_WIDTH])) entity_gradients.append(list(x[0:self.ENTITY_WIDTH]))
# print("doc_gradient", doc_gradient) # print("doc_gradient", doc_gradient)
# print("entity_gradients", entity_gradients) # print("entity_gradients", entity_gradients)
bp_doc([doc_gradient], sgd=self.sgd_article) bp_doc([doc_gradient], sgd=self.sgd_article)
bp_entity(entity_gradients, sgd=self.sgd_entity) bp_entity(entity_gradients, sgd=self.sgd_entity)
def _get_training_data(self, training_dir, entity_descr_output, dev, limit, balance, to_print): 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) 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 pos_entities[article_id + "_" + mention] = descr
for mention, entity_negs in incorrect_entries[article_id].items(): for mention, entity_negs in incorrect_entries[article_id].items():
neg_count = 0 if not balance or pos_entities.get(article_id + "_" + mention):
for entity_neg in entity_negs: neg_count = 0
descr = id_to_descr.get(entity_neg) for entity_neg in entity_negs:
if descr:
# if balance, keep only 1 negative instance for each positive instance # if balance, keep only 1 negative instance for each positive instance
if neg_count < 1 or not balance: if neg_count < 1 or not balance:
descr_list = neg_entities.get(article_id + "_" + mention, []) descr = id_to_descr.get(entity_neg)
descr_list.append(descr) if descr:
neg_entities[article_id + "_" + mention] = descr_list descr_list = neg_entities.get(article_id + "_" + mention, [])
neg_count += 1 descr_list.append(descr)
neg_entities[article_id + "_" + mention] = descr_list
neg_count += 1
if to_print: if to_print:
print() print()

2
examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
View File

@ -111,7 +111,7 @@ if __name__ == "__main__":
print("STEP 6: training", datetime.datetime.now()) print("STEP 6: training", datetime.datetime.now())
my_nlp = spacy.load('en_core_web_md') my_nlp = spacy.load('en_core_web_md')
trainer = EL_Model(kb=my_kb, nlp=my_nlp) 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() print()
# STEP 7: apply the EL algorithm on the dev dataset # STEP 7: apply the EL algorithm on the dev dataset