# coding: utf-8 from __future__ import unicode_literals import os import datetime from os import listdir import numpy as np import random from thinc.neural._classes.convolution import ExtractWindow from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator from spacy._ml import SpacyVectors, create_default_optimizer, zero_init, logistic from thinc.api import chain, concatenate, flatten_add_lengths, clone from thinc.v2v import Model, Maxout, Affine from thinc.t2v import Pooling, mean_pool from thinc.t2t import ParametricAttention from thinc.misc import Residual from thinc.misc import LayerNorm as LN from spacy.tokens import Doc """ TODO: this code needs to be implemented in pipes.pyx""" class EL_Model: PRINT_LOSS = False PRINT_F = True EPS = 0.0000000005 CUTOFF = 0.5 INPUT_DIM = 300 ENTITY_WIDTH = 64 ARTICLE_WIDTH = 128 HIDDEN_WIDTH = 64 name = "entity_linker" def __init__(self, kb, nlp): run_el._prepare_pipeline(nlp, kb) self.nlp = nlp self.kb = kb self._build_cnn(hidden_entity_width=self.ENTITY_WIDTH, hidden_article_width=self.ARTICLE_WIDTH) def train_model(self, training_dir, entity_descr_output, trainlimit=None, devlimit=None, to_print=True): # raise errors instead of runtime warnings in case of int/float overflow np.seterr(all='raise') Doc.set_extension("entity_id", default=None) train_inst, train_pos, train_neg, train_doc = self._get_training_data(training_dir, entity_descr_output, False, trainlimit, to_print=False) dev_inst, dev_pos, dev_neg, dev_doc = self._get_training_data(training_dir, entity_descr_output, True, devlimit, to_print=False) self._begin_training() print() self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_random", calc_random=True) self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_random", calc_random=True) print() self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_pre", calc_random=False) self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_pre", avg=False) instance_pos_count = 0 instance_neg_count = 0 if to_print: print() print("Training on", len(train_inst.values()), "articles") print("Dev test on", len(dev_inst.values()), "articles") # TODO: proper batches. Currently 1 article at the time article_count = 0 for article_id, inst_cluster_set in train_inst.items(): # if to_print: # print() # print(article_count, "Training on article", article_id) article_count += 1 article_docs = list() entities = list() golds = list() for inst_cluster in inst_cluster_set: article_docs.append(train_doc[article_id]) entities.append(train_pos.get(inst_cluster)) golds.append(float(1.0)) instance_pos_count += 1 for neg_entity in train_neg.get(inst_cluster, []): article_docs.append(train_doc[article_id]) entities.append(neg_entity) golds.append(float(0.0)) instance_neg_count += 1 self.update(article_docs=article_docs, entities=entities, golds=golds) # dev eval self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_inter", avg=False) if to_print: print() print("Trained on", instance_pos_count, "/", instance_neg_count, "instances pos/neg") print() self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_post", calc_random=False) self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_post", avg=False) def _test_dev(self, instances, pos, neg, doc, print_string, avg=False, calc_random=False): predictions = list() golds = list() for article_id, inst_cluster_set in instances.items(): for inst_cluster in inst_cluster_set: pos_ex = pos.get(inst_cluster) neg_exs = neg.get(inst_cluster, []) article = inst_cluster.split(sep="_")[0] entity_id = inst_cluster.split(sep="_")[1] article_doc = doc[article] if calc_random: prediction = self._predict_random(entity=pos_ex) else: prediction = self._predict(article_doc=article_doc, entity=pos_ex, avg=avg) predictions.append(prediction) golds.append(float(1.0)) for neg_ex in neg_exs: if calc_random: prediction = self._predict_random(entity=neg_ex) else: prediction = self._predict(article_doc=article_doc, entity=neg_ex, avg=avg) predictions.append(prediction) golds.append(float(0.0)) # TODO: combine with prior probability p, r, f = run_el.evaluate(predictions, golds, to_print=False) if self.PRINT_F: # print("p/r/F", print_string, round(p, 1), round(r, 1), round(f, 1)) print("F", print_string, round(f, 1)) loss, d_scores = self.get_loss(self.model.ops.asarray(predictions), self.model.ops.asarray(golds)) if self.PRINT_LOSS: print("loss", print_string, round(loss, 5)) return loss, p, r, f def _predict(self, article_doc, entity, avg=False, apply_threshold=True): if avg: with self.sgd.use_params(self.model.averages): doc_encoding = self.article_encoder([article_doc]) entity_encoding = self.entity_encoder([entity]) return self.model(np.append(entity_encoding, doc_encoding)) # TODO list doc_encoding = self.article_encoder([article_doc])[0] entity_encoding = self.entity_encoder([entity])[0] concat_encoding = list(entity_encoding) + list(doc_encoding) np_array = np.asarray([concat_encoding]) prediction = self.model(np_array) if not apply_threshold: return float(prediction) if prediction > self.CUTOFF: return float(1.0) return float(0.0) def _predict_random(self, entity, apply_threshold=True): r = random.uniform(0, 1) if not apply_threshold: return r if r > self.CUTOFF: return float(1.0) return float(0.0) def _build_cnn(self, hidden_entity_width, hidden_article_width): with Model.define_operators({">>": chain, "|": concatenate, "**": clone}): self.entity_encoder = self._encoder(in_width=self.INPUT_DIM, hidden_width=hidden_entity_width) self.article_encoder = self._encoder(in_width=self.INPUT_DIM, hidden_width=hidden_article_width) nr_i = hidden_entity_width + hidden_article_width nr_o = self.HIDDEN_WIDTH self.model = Affine(nr_o, nr_i) \ >> LN(Maxout(nr_o, nr_o)) \ >> Affine(1, nr_o) \ >> logistic @staticmethod def _encoder(in_width, hidden_width): with Model.define_operators({">>": chain}): encoder = SpacyVectors \ >> flatten_add_lengths \ >> ParametricAttention(in_width)\ >> Pooling(mean_pool) \ >> Residual((ExtractWindow(nW=1) >> LN(Maxout(in_width, in_width * 3)))) \ >> zero_init(Affine(hidden_width, in_width, drop_factor=0.0)) # TODO: ReLu instead of LN(Maxout) ? return encoder def _begin_training(self): self.sgd = create_default_optimizer(self.model.ops) @staticmethod def get_loss(predictions, golds): d_scores = (predictions - golds) loss = (d_scores ** 2).sum() return loss, d_scores def update(self, article_docs, entities, golds, drop=0., apply_threshold=True): doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=drop) entity_encodings, bp_encoding = self.entity_encoder.begin_update(entities, drop=drop) concat_encodings = [list(entity_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))] predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=drop) predictions = self.model.ops.flatten(predictions) golds = self.model.ops.asarray(golds) loss, d_scores = self.get_loss(predictions, golds) # if self.PRINT_LOSS: # print("loss train", round(loss, 5)) # if self.PRINT_F: # predictions_f = [x for x in predictions] # if apply_threshold: # predictions_f = [1.0 if x > self.CUTOFF else 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)) d_scores = d_scores.astype(np.float32) model_gradient = bp_model(d_scores, sgd=self.sgd) doc_gradient = [x[0:self.ARTICLE_WIDTH] for x in model_gradient] entity_gradient = [x[self.ARTICLE_WIDTH:] for x in model_gradient] bp_doc(doc_gradient) bp_encoding(entity_gradient) def _get_training_data(self, training_dir, entity_descr_output, dev, 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) instance_by_doc = dict() local_vectors = list() # TODO: local vectors doc_by_article = dict() pos_entities = dict() neg_entities = dict() cnt = 0 for f in listdir(training_dir): if not limit or cnt < limit: if dev == 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 training 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 instance_by_doc[article_id] = set() for mention, entity_pos in correct_entries[article_id].items(): descr = id_to_descr.get(entity_pos) if descr: instance_by_doc[article_id].add(article_id + "_" + mention) doc_descr = self.nlp(descr) doc_descr._.entity_id = entity_pos pos_entities[article_id + "_" + mention] = doc_descr for mention, 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 = self.nlp(descr) doc_descr._.entity_id = entity_neg descr_list = neg_entities.get(article_id + "_" + mention, []) descr_list.append(doc_descr) neg_entities[article_id + "_" + mention] = descr_list if to_print: print() print("Processed", cnt, "training articles, dev=" + str(dev)) print() return instance_by_doc, pos_entities, neg_entities, doc_by_article