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60% acc run
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svlandeg
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268a52ead7
commit
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@ -23,7 +23,6 @@ from thinc.misc import LayerNorm as LN
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# from spacy.cli.pretrain import get_cossim_loss
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from spacy.matcher import PhraseMatcher
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from spacy.tokens import Doc
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""" TODO: this code needs to be implemented in pipes.pyx"""
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@ -46,7 +45,7 @@ class EL_Model:
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DROP = 0.1
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LEARN_RATE = 0.001
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EPOCHS = 10
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EPOCHS = 20
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L2 = 1e-6
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name = "entity_linker"
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@ -211,9 +210,6 @@ class EL_Model:
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return acc
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def _predict(self, article_doc, sent_doc, desc_docs, avg=True, apply_threshold=True):
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# print()
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# print("predicting article")
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if avg:
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with self.article_encoder.use_params(self.sgd_article.averages) \
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and self.desc_encoder.use_params(self.sgd_desc.averages)\
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@ -228,16 +224,10 @@ class EL_Model:
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doc_encoding = self.article_encoder([article_doc])
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sent_encoding = self.sent_encoder([sent_doc])
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# print("desc_encodings", desc_encodings)
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# print("doc_encoding", doc_encoding)
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# print("sent_encoding", sent_encoding)
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concat_encoding = [list(doc_encoding[0]) + list(sent_encoding[0])]
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# print("concat_encoding", concat_encoding)
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cont_encodings = self.cont_encoder(np.asarray([concat_encoding[0]]))
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# print("cont_encodings", cont_encodings)
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context_enc = np.transpose(cont_encodings)
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# print("context_enc", context_enc)
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highest_sim = -5
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best_i = -1
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@ -353,11 +343,11 @@ class EL_Model:
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sents_list.append(sent)
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descs_list.append(descs[e])
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targets.append([1])
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else:
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arts_list.append(art)
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sents_list.append(sent)
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descs_list.append(descs[e])
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targets.append([-1])
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# else:
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# arts_list.append(art)
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# sents_list.append(sent)
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# descs_list.append(descs[e])
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# targets.append([-1])
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desc_docs = self.nlp.pipe(descs_list)
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desc_encodings, bp_desc = self.desc_encoder.begin_update(desc_docs, drop=self.DROP)
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@ -372,18 +362,17 @@ class EL_Model:
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range(len(targets))]
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cont_encodings, bp_cont = self.cont_encoder.begin_update(np.asarray(concat_encodings), drop=self.DROP)
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# print("sent_encodings", type(sent_encodings), sent_encodings)
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# print("desc_encodings", type(desc_encodings), desc_encodings)
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# print("doc_encodings", type(doc_encodings), doc_encodings)
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# print("getting los for", len(arts_list), "entities")
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loss, cont_gradient = self.get_loss(cont_encodings, desc_encodings, targets)
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loss, gradient = self.get_loss(cont_encodings, desc_encodings, targets)
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# loss, desc_gradient = self.get_loss(desc_encodings, cont_encodings, targets)
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# cont_gradient = cont_gradient / 2
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# desc_gradient = desc_gradient / 2
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# bp_desc(desc_gradient, sgd=self.sgd_desc)
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# print("gradient", gradient)
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if self.PRINT_BATCH_LOSS:
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print("batch loss", loss)
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context_gradient = bp_cont(gradient, sgd=self.sgd_cont)
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context_gradient = bp_cont(cont_gradient, sgd=self.sgd_cont)
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# gradient : concat (doc+sent) vs. desc
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sent_start = self.ARTICLE_WIDTH
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@ -393,9 +382,6 @@ class EL_Model:
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doc_gradients.append(list(x[0:sent_start]))
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sent_gradients.append(list(x[sent_start:]))
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# print("doc_gradients", doc_gradients)
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# print("sent_gradients", sent_gradients)
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bp_doc(doc_gradients, sgd=self.sgd_article)
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bp_sent(sent_gradients, sgd=self.sgd_sent)
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@ -426,74 +412,75 @@ class EL_Model:
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article_id = f.replace(".txt", "")
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if cnt % 500 == 0 and to_print:
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print(datetime.datetime.now(), "processed", cnt, "files in the training dataset")
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cnt += 1
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# parse the article text
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with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
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text = file.read()
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article_doc = self.nlp(text)
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truncated_text = text[0:min(self.DOC_CUTOFF, len(text))]
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text_by_article[article_id] = truncated_text
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try:
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# parse the article text
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with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
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text = file.read()
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article_doc = self.nlp(text)
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truncated_text = text[0:min(self.DOC_CUTOFF, len(text))]
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text_by_article[article_id] = truncated_text
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# process all positive and negative entities, collect all relevant mentions in this article
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for mention, entity_pos in correct_entries[article_id].items():
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cluster = article_id + "_" + mention
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descr = id_to_descr.get(entity_pos)
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entities = set()
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if descr:
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entity = "E_" + str(next_entity_nr) + "_" + cluster
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next_entity_nr += 1
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gold_by_entity[entity] = 1
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desc_by_entity[entity] = descr
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entities.add(entity)
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# process all positive and negative entities, collect all relevant mentions in this article
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for mention, entity_pos in correct_entries[article_id].items():
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cluster = article_id + "_" + mention
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descr = id_to_descr.get(entity_pos)
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entities = set()
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if descr:
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entity = "E_" + str(next_entity_nr) + "_" + cluster
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next_entity_nr += 1
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gold_by_entity[entity] = 1
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desc_by_entity[entity] = descr
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entities.add(entity)
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entity_negs = incorrect_entries[article_id][mention]
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for entity_neg in entity_negs:
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descr = id_to_descr.get(entity_neg)
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if descr:
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entity = "E_" + str(next_entity_nr) + "_" + cluster
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next_entity_nr += 1
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gold_by_entity[entity] = 0
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desc_by_entity[entity] = descr
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entities.add(entity)
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entity_negs = incorrect_entries[article_id][mention]
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for entity_neg in entity_negs:
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descr = id_to_descr.get(entity_neg)
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if descr:
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entity = "E_" + str(next_entity_nr) + "_" + cluster
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next_entity_nr += 1
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gold_by_entity[entity] = 0
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desc_by_entity[entity] = descr
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entities.add(entity)
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found_matches = 0
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if len(entities) > 1:
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entities_by_cluster[cluster] = entities
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found_matches = 0
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if len(entities) > 1:
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entities_by_cluster[cluster] = entities
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# find all matches in the doc for the mentions
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# TODO: fix this - doesn't look like all entities are found
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matcher = PhraseMatcher(self.nlp.vocab)
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patterns = list(self.nlp.tokenizer.pipe([mention]))
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# find all matches in the doc for the mentions
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# TODO: fix this - doesn't look like all entities are found
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matcher = PhraseMatcher(self.nlp.vocab)
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patterns = list(self.nlp.tokenizer.pipe([mention]))
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matcher.add("TerminologyList", None, *patterns)
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matches = matcher(article_doc)
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matcher.add("TerminologyList", None, *patterns)
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matches = matcher(article_doc)
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# store sentences
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for match_id, start, end in matches:
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span = article_doc[start:end]
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if mention == span.text:
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found_matches += 1
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sent_text = span.sent.text
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sent_nr = sentence_by_text.get(sent_text, None)
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if sent_nr is None:
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sent_nr = "S_" + str(next_sent_nr) + article_id
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next_sent_nr += 1
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text_by_sentence[sent_nr] = sent_text
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sentence_by_text[sent_text] = sent_nr
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article_by_cluster[cluster] = article_id
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sentence_by_cluster[cluster] = sent_nr
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# store sentences
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for match_id, start, end in matches:
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found_matches += 1
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span = article_doc[start:end]
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assert mention == span.text
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sent_text = span.sent.text
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sent_nr = sentence_by_text.get(sent_text, None)
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if sent_nr is None:
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sent_nr = "S_" + str(next_sent_nr) + article_id
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next_sent_nr += 1
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text_by_sentence[sent_nr] = sent_text
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sentence_by_text[sent_text] = sent_nr
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article_by_cluster[cluster] = article_id
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sentence_by_cluster[cluster] = sent_nr
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if found_matches == 0:
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# TODO print("Could not find neg instances or sentence matches for", mention, "in", article_id)
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entities_by_cluster.pop(cluster, None)
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article_by_cluster.pop(cluster, None)
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sentence_by_cluster.pop(cluster, None)
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for entity in entities:
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gold_by_entity.pop(entity, None)
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desc_by_entity.pop(entity, None)
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if found_matches == 0:
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# print("Could not find neg instances or sentence matches for", mention, "in", article_id)
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entities_by_cluster.pop(cluster, None)
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article_by_cluster.pop(cluster, None)
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sentence_by_cluster.pop(cluster, None)
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for entity in entities:
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gold_by_entity.pop(entity, None)
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desc_by_entity.pop(entity, None)
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cnt += 1
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except:
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print("Problem parsing article", article_id)
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if to_print:
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print()
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@ -111,7 +111,7 @@ if __name__ == "__main__":
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print("STEP 6: training", datetime.datetime.now())
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my_nlp = spacy.load('en_core_web_md')
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trainer = EL_Model(kb=my_kb, nlp=my_nlp)
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trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1000, devlimit=100)
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trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=10000, devlimit=500)
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print()
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# STEP 7: apply the EL algorithm on the dev dataset
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@ -120,7 +120,6 @@ if __name__ == "__main__":
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run_el.run_el_dev(kb=my_kb, nlp=my_nlp, training_dir=TRAINING_DIR, limit=2000)
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print()
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# TODO coreference resolution
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# add_coref()
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