# coding: utf-8 from __future__ import unicode_literals import os import datetime from os import listdir import numpy as np import random from random import shuffle 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, with_flatten from thinc.v2v import Model, Maxout, Affine, ReLu from thinc.t2v import Pooling, mean_pool, sum_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_TRAIN = False EPS = 0.0000000005 CUTOFF = 0.5 BATCH_SIZE = 5 INPUT_DIM = 300 HIDDEN_1_WIDTH = 32 # 10 HIDDEN_2_WIDTH = 32 # 6 DESC_WIDTH = 64 # 4 ARTICLE_WIDTH = 64 # 8 DROP = 0.1 name = "entity_linker" def __init__(self, kb, nlp): run_el._prepare_pipeline(nlp, kb) self.nlp = nlp self.kb = kb self._build_cnn(in_width=self.INPUT_DIM, desc_width=self.DESC_WIDTH, article_width=self.ARTICLE_WIDTH, hidden_1_width=self.HIDDEN_1_WIDTH, hidden_2_width=self.HIDDEN_2_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') train_ent, train_gold, train_desc, train_article, train_texts = self._get_training_data(training_dir, entity_descr_output, False, trainlimit, to_print=False) train_pos_entities = [k for k,v in train_gold.items() if v] train_neg_entities = [k for k,v in train_gold.items() if not v] train_pos_count = len(train_pos_entities) train_neg_count = len(train_neg_entities) # upsample positives to 50-50 distribution while train_pos_count < train_neg_count: train_ent.append(random.choice(train_pos_entities)) train_pos_count += 1 # upsample negatives to 50-50 distribution while train_neg_count < train_pos_count: train_ent.append(random.choice(train_neg_entities)) train_neg_count += 1 shuffle(train_ent) dev_ent, dev_gold, dev_desc, dev_article, dev_texts = self._get_training_data(training_dir, entity_descr_output, True, devlimit, to_print=False) shuffle(dev_ent) dev_pos_count = len([g for g in dev_gold.values() if g]) dev_neg_count = len([g for g in dev_gold.values() if not g]) self._begin_training() print() self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_random", calc_random=True) print() self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_pre", avg=True) if to_print: print() print("Training on", len(train_ent), "entities in", len(train_texts), "articles") print("Training instances pos/neg", train_pos_count, train_neg_count) print() print("Dev test on", len(dev_ent), "entities in", len(dev_texts), "articles") print("Dev instances pos/neg", dev_pos_count, dev_neg_count) print() print(" CUTOFF", self.CUTOFF) print(" INPUT_DIM", self.INPUT_DIM) print(" HIDDEN_1_WIDTH", self.HIDDEN_1_WIDTH) print(" DESC_WIDTH", self.DESC_WIDTH) print(" ARTICLE_WIDTH", self.ARTICLE_WIDTH) print(" HIDDEN_2_WIDTH", self.HIDDEN_2_WIDTH) print(" DROP", self.DROP) print() start = 0 stop = min(self.BATCH_SIZE, len(train_ent)) processed = 0 while start < len(train_ent): next_batch = train_ent[start:stop] golds = [train_gold[e] for e in next_batch] descs = [train_desc[e] for e in next_batch] articles = [train_texts[train_article[e]] for e in next_batch] self.update(entities=next_batch, golds=golds, descs=descs, texts=articles) self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_inter", avg=True) processed += len(next_batch) start = start + self.BATCH_SIZE stop = min(stop + self.BATCH_SIZE, len(train_ent)) if to_print: print() print("Trained on", processed, "entities in total") def _test_dev(self, entities, gold_by_entity, desc_by_entity, article_by_entity, texts_by_id, print_string, avg=True, calc_random=False): golds = [gold_by_entity[e] for e in entities] if calc_random: predictions = self._predict_random(entities=entities) else: desc_docs = self.nlp.pipe([desc_by_entity[e] for e in entities]) article_docs = self.nlp.pipe([texts_by_id[article_by_entity[e]] for e in entities]) predictions = self._predict(entities=entities, article_docs=article_docs, desc_docs=desc_docs, avg=avg) # TODO: combine with prior probability p, r, f, acc = run_el.evaluate(predictions, golds, to_print=False) loss, gradient = self.get_loss(self.model.ops.asarray(predictions), self.model.ops.asarray(golds)) print("p/r/F/acc/loss", print_string, round(p, 1), round(r, 1), round(f, 1), round(acc, 2), round(loss, 5)) return loss, p, r, f def _predict(self, entities, article_docs, desc_docs, avg=True, apply_threshold=True): if avg: with self.article_encoder.use_params(self.sgd_article.averages) \ and self.desc_encoder.use_params(self.sgd_entity.averages): doc_encodings = self.article_encoder(article_docs) desc_encodings = self.desc_encoder(desc_docs) else: doc_encodings = self.article_encoder(article_docs) desc_encodings = self.desc_encoder(desc_docs) concat_encodings = [list(desc_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))] np_array_list = np.asarray(concat_encodings) if avg: with self.model.use_params(self.sgd.averages): predictions = self.model(np_array_list) else: predictions = self.model(np_array_list) predictions = self.model.ops.flatten(predictions) predictions = [float(p) for p in predictions] if apply_threshold: predictions = [float(1.0) if p > self.CUTOFF else float(0.0) for p in predictions] return predictions def _predict_random(self, entities, apply_threshold=True): if not apply_threshold: return [float(random.uniform(0, 1)) for e in entities] else: return [float(1.0) if random.uniform(0, 1) > self.CUTOFF else float(0.0) for e in entities] def _build_cnn(self, in_width, desc_width, article_width, hidden_1_width, hidden_2_width): with Model.define_operators({">>": chain, "|": concatenate, "**": clone}): self.desc_encoder = self._encoder(in_width=in_width, hidden_with=hidden_1_width, end_width=desc_width) self.article_encoder = self._encoder(in_width=in_width, hidden_with=hidden_1_width, end_width=article_width) in_width = desc_width + article_width out_width = hidden_2_width self.model = Affine(out_width, in_width) \ >> LN(Maxout(out_width, out_width)) \ >> Affine(1, out_width) \ >> logistic @staticmethod def _encoder(in_width, hidden_with, end_width): conv_depth = 2 cnn_maxout_pieces = 3 with Model.define_operators({">>": chain}): convolution = Residual((ExtractWindow(nW=1) >> LN(Maxout(hidden_with, hidden_with * 3, pieces=cnn_maxout_pieces)))) encoder = SpacyVectors \ >> with_flatten(LN(Maxout(hidden_with, in_width)) >> convolution ** conv_depth, pad=conv_depth) \ >> flatten_add_lengths \ >> ParametricAttention(hidden_with)\ >> Pooling(mean_pool) \ >> Residual(zero_init(Maxout(hidden_with, hidden_with))) \ >> zero_init(Affine(end_width, hidden_with, drop_factor=0.0)) # TODO: ReLu or LN(Maxout) ? # sum_pool or mean_pool ? return encoder def _begin_training(self): self.sgd_article = create_default_optimizer(self.article_encoder.ops) self.sgd_entity = create_default_optimizer(self.desc_encoder.ops) self.sgd = create_default_optimizer(self.model.ops) @staticmethod def get_loss(predictions, golds): d_scores = (predictions - golds) gradient = d_scores.mean() loss = (d_scores ** 2).mean() return loss, gradient def update(self, entities, golds, descs, texts): golds = self.model.ops.asarray(golds) desc_docs = self.nlp.pipe(descs) article_docs = self.nlp.pipe(texts) doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=self.DROP) desc_encodings, bp_entity = self.desc_encoder.begin_update(desc_docs, drop=self.DROP) concat_encodings = [list(desc_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))] predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP) predictions = self.model.ops.flatten(predictions) # print("entities", entities) # print("predictions", predictions) # print("golds", golds) loss, gradient = self.get_loss(predictions, golds) if self.PRINT_TRAIN: print("loss train", round(loss, 5)) gradient = float(gradient) # print("gradient", gradient) # print("loss", loss) model_gradient = bp_model(gradient, sgd=self.sgd) # print("model_gradient", model_gradient) # concat = desc + doc, but doc is the same within this function (TODO: multiple docs/articles) doc_gradient = model_gradient[0][self.DESC_WIDTH:] entity_gradients = list() for x in model_gradient: entity_gradients.append(list(x[0:self.DESC_WIDTH])) # 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) 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) local_vectors = list() # TODO: local vectors text_by_article = dict() gold_by_entity = dict() desc_by_entity = dict() article_by_entity = dict() entities = list() cnt = 0 next_entity_nr = 0 files = listdir(training_dir) shuffle(files) for f in files: 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 text_by_article: with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file: text = file.read() text_by_article[article_id] = text for mention, entity_pos in correct_entries[article_id].items(): descr = id_to_descr.get(entity_pos) if descr: entities.append(next_entity_nr) gold_by_entity[next_entity_nr] = 1 desc_by_entity[next_entity_nr] = descr article_by_entity[next_entity_nr] = article_id next_entity_nr += 1 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: entities.append(next_entity_nr) gold_by_entity[next_entity_nr] = 0 desc_by_entity[next_entity_nr] = descr article_by_entity[next_entity_nr] = article_id next_entity_nr += 1 if to_print: print() print("Processed", cnt, "training articles, dev=" + str(dev)) print() return entities, gold_by_entity, desc_by_entity, article_by_entity, text_by_article