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404 lines
17 KiB
Python
404 lines
17 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import os
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import datetime
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from os import listdir
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import numpy as np
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import random
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from random import shuffle
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from thinc.neural._classes.convolution import ExtractWindow
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from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
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from spacy._ml import SpacyVectors, create_default_optimizer, zero_init, logistic
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from thinc.api import chain, concatenate, flatten_add_lengths, clone, with_flatten
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from thinc.v2v import Model, Maxout, Affine, ReLu
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from thinc.t2v import Pooling, mean_pool, sum_pool
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from thinc.t2t import ParametricAttention
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from thinc.misc import Residual
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from thinc.misc import LayerNorm as LN
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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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class EL_Model:
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PRINT_INSPECT = False
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PRINT_TRAIN = False
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EPS = 0.0000000005
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CUTOFF = 0.5
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BATCH_SIZE = 5
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DOC_CUTOFF = 300 # number of characters from the doc context
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INPUT_DIM = 300 # dimension of pre-trained vectors
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HIDDEN_1_WIDTH = 32 # 10
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HIDDEN_2_WIDTH = 32 # 6
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DESC_WIDTH = 64 # 4
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ARTICLE_WIDTH = 64 # 8
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DROP = 0.1
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name = "entity_linker"
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def __init__(self, kb, nlp):
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run_el._prepare_pipeline(nlp, kb)
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self.nlp = nlp
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self.kb = kb
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self._build_cnn(in_width=self.INPUT_DIM,
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desc_width=self.DESC_WIDTH,
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article_width=self.ARTICLE_WIDTH,
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hidden_1_width=self.HIDDEN_1_WIDTH,
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hidden_2_width=self.HIDDEN_2_WIDTH)
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def train_model(self, training_dir, entity_descr_output, trainlimit=None, devlimit=None, to_print=True):
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# raise errors instead of runtime warnings in case of int/float overflow
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np.seterr(all='raise')
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train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts = \
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self._get_training_data(training_dir, entity_descr_output, False, trainlimit, to_print=False)
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# inspect data
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if self.PRINT_INSPECT:
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for entity in train_ent:
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print("entity", entity)
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print("gold", train_gold[entity])
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print("desc", train_desc[entity])
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print("sentence ID", train_sent[entity])
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print("sentence text", train_sent_texts[train_sent[entity]])
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print("article ID", train_art[entity])
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print("article text", train_art_texts[train_art[entity]])
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print()
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train_pos_entities = [k for k,v in train_gold.items() if v]
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train_neg_entities = [k for k,v in train_gold.items() if not v]
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train_pos_count = len(train_pos_entities)
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train_neg_count = len(train_neg_entities)
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if to_print:
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print()
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print("Upsampling, original training instances pos/neg:", train_pos_count, train_neg_count)
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# upsample positives to 50-50 distribution
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while train_pos_count < train_neg_count:
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train_ent.append(random.choice(train_pos_entities))
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train_pos_count += 1
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# upsample negatives to 50-50 distribution
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while train_neg_count < train_pos_count:
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train_ent.append(random.choice(train_neg_entities))
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train_neg_count += 1
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shuffle(train_ent)
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dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, dev_sent, dev_sent_texts = \
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self._get_training_data(training_dir, entity_descr_output, True, devlimit, to_print=False)
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shuffle(dev_ent)
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dev_pos_count = len([g for g in dev_gold.values() if g])
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dev_neg_count = len([g for g in dev_gold.values() if not g])
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self._begin_training()
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if to_print:
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print()
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print("Training on", len(train_ent), "entities in", len(train_art_texts), "articles")
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print("Training instances pos/neg:", train_pos_count, train_neg_count)
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print()
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print("Dev test on", len(dev_ent), "entities in", len(dev_art_texts), "articles")
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print("Dev instances pos/neg:", dev_pos_count, dev_neg_count)
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print()
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print(" CUTOFF", self.CUTOFF)
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print(" DOC_CUTOFF", self.DOC_CUTOFF)
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print(" INPUT_DIM", self.INPUT_DIM)
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print(" HIDDEN_1_WIDTH", self.HIDDEN_1_WIDTH)
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print(" DESC_WIDTH", self.DESC_WIDTH)
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print(" ARTICLE_WIDTH", self.ARTICLE_WIDTH)
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print(" HIDDEN_2_WIDTH", self.HIDDEN_2_WIDTH)
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print(" DROP", self.DROP)
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print()
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self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, print_string="dev_random", calc_random=True)
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self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, print_string="dev_pre", avg=True)
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print()
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start = 0
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stop = min(self.BATCH_SIZE, len(train_ent))
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processed = 0
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while start < len(train_ent):
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next_batch = train_ent[start:stop]
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golds = [train_gold[e] for e in next_batch]
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descs = [train_desc[e] for e in next_batch]
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articles = [train_art_texts[train_art[e]] for e in next_batch]
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self.update(entities=next_batch, golds=golds, descs=descs, texts=articles)
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self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, print_string="dev_inter", avg=True)
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processed += len(next_batch)
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start = start + self.BATCH_SIZE
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stop = min(stop + self.BATCH_SIZE, len(train_ent))
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if to_print:
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print()
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print("Trained on", processed, "entities in total")
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def _test_dev(self, entities, gold_by_entity, desc_by_entity, article_by_entity, texts_by_id, print_string, avg=True, calc_random=False):
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golds = [gold_by_entity[e] for e in entities]
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if calc_random:
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predictions = self._predict_random(entities=entities)
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else:
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desc_docs = self.nlp.pipe([desc_by_entity[e] for e in entities])
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article_docs = self.nlp.pipe([texts_by_id[article_by_entity[e]] for e in entities])
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predictions = self._predict(entities=entities, article_docs=article_docs, desc_docs=desc_docs, avg=avg)
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# TODO: combine with prior probability
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p, r, f, acc = run_el.evaluate(predictions, golds, to_print=False, times_hundred=False)
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loss, gradient = self.get_loss(self.model.ops.asarray(predictions), self.model.ops.asarray(golds))
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print("p/r/F/acc/loss", print_string, round(p, 1), round(r, 1), round(f, 1), round(acc, 2), round(loss, 5))
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return loss, p, r, f
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def _predict(self, entities, article_docs, desc_docs, avg=True, apply_threshold=True):
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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_entity.averages):
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doc_encodings = self.article_encoder(article_docs)
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desc_encodings = self.desc_encoder(desc_docs)
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else:
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doc_encodings = self.article_encoder(article_docs)
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desc_encodings = self.desc_encoder(desc_docs)
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concat_encodings = [list(desc_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))]
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np_array_list = np.asarray(concat_encodings)
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if avg:
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with self.model.use_params(self.sgd.averages):
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predictions = self.model(np_array_list)
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else:
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predictions = self.model(np_array_list)
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predictions = self.model.ops.flatten(predictions)
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predictions = [float(p) for p in predictions]
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if apply_threshold:
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predictions = [float(1.0) if p > self.CUTOFF else float(0.0) for p in predictions]
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return predictions
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def _predict_random(self, entities, apply_threshold=True):
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if not apply_threshold:
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return [float(random.uniform(0, 1)) for e in entities]
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else:
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return [float(1.0) if random.uniform(0, 1) > self.CUTOFF else float(0.0) for e in entities]
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def _build_cnn(self, in_width, desc_width, article_width, hidden_1_width, hidden_2_width):
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with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
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self.desc_encoder = self._encoder(in_width=in_width, hidden_with=hidden_1_width, end_width=desc_width)
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self.article_encoder = self._encoder(in_width=in_width, hidden_with=hidden_1_width, end_width=article_width)
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in_width = desc_width + article_width
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out_width = hidden_2_width
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self.model = Affine(out_width, in_width) \
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>> LN(Maxout(out_width, out_width)) \
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>> Affine(1, out_width) \
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>> logistic
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@staticmethod
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def _encoder(in_width, hidden_with, end_width):
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conv_depth = 2
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cnn_maxout_pieces = 3
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with Model.define_operators({">>": chain}):
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convolution = Residual((ExtractWindow(nW=1) >> LN(Maxout(hidden_with, hidden_with * 3, pieces=cnn_maxout_pieces))))
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encoder = SpacyVectors \
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>> with_flatten(LN(Maxout(hidden_with, in_width)) >> convolution ** conv_depth, pad=conv_depth) \
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>> flatten_add_lengths \
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>> ParametricAttention(hidden_with)\
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>> Pooling(mean_pool) \
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>> Residual(zero_init(Maxout(hidden_with, hidden_with))) \
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>> zero_init(Affine(end_width, hidden_with, drop_factor=0.0))
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# TODO: ReLu or LN(Maxout) ?
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# sum_pool or mean_pool ?
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return encoder
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def _begin_training(self):
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self.sgd_article = create_default_optimizer(self.article_encoder.ops)
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self.sgd_entity = create_default_optimizer(self.desc_encoder.ops)
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self.sgd = create_default_optimizer(self.model.ops)
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@staticmethod
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def get_loss(predictions, golds):
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d_scores = (predictions - golds)
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gradient = d_scores.mean()
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loss = (d_scores ** 2).mean()
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return loss, gradient
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def update(self, entities, golds, descs, texts):
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golds = self.model.ops.asarray(golds)
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desc_docs = self.nlp.pipe(descs)
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article_docs = self.nlp.pipe(texts)
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doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=self.DROP)
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desc_encodings, bp_entity = self.desc_encoder.begin_update(desc_docs, drop=self.DROP)
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concat_encodings = [list(desc_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))]
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predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP)
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predictions = self.model.ops.flatten(predictions)
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# print("entities", entities)
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# print("predictions", predictions)
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# print("golds", golds)
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loss, gradient = self.get_loss(predictions, golds)
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if self.PRINT_TRAIN:
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print("loss train", round(loss, 5))
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gradient = float(gradient)
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# print("gradient", gradient)
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# print("loss", loss)
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model_gradient = bp_model(gradient, sgd=self.sgd)
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# print("model_gradient", model_gradient)
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# concat = desc + doc, but doc is the same within this function (TODO: multiple docs/articles)
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doc_gradient = model_gradient[0][self.DESC_WIDTH:]
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entity_gradients = list()
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for x in model_gradient:
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entity_gradients.append(list(x[0:self.DESC_WIDTH]))
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# print("doc_gradient", doc_gradient)
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# print("entity_gradients", entity_gradients)
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bp_doc([doc_gradient], sgd=self.sgd_article)
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bp_entity(entity_gradients, sgd=self.sgd_entity)
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def _get_training_data(self, training_dir, entity_descr_output, dev, limit, to_print):
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id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
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correct_entries, incorrect_entries = training_set_creator.read_training_entities(training_output=training_dir,
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collect_correct=True,
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collect_incorrect=True)
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local_vectors = list() # TODO: local vectors
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entities = set()
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gold_by_entity = dict()
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desc_by_entity = dict()
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article_by_entity = dict()
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text_by_article = dict()
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sentence_by_entity = dict()
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text_by_sentence = dict()
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cnt = 0
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next_entity_nr = 1
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next_sent_nr = 1
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files = listdir(training_dir)
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shuffle(files)
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for f in files:
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if not limit or cnt < limit:
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if dev == run_el.is_dev(f):
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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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# process all positive and negative entities, collect all relevant mentions in this article
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article_terms = set()
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entities_by_mention = dict()
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for mention, entity_pos in correct_entries[article_id].items():
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descr = id_to_descr.get(entity_pos)
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if descr:
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entity = "E_" + str(next_entity_nr) + "_" + article_id + "_" + mention
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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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article_terms.add(mention)
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mention_entities = entities_by_mention.get(mention, set())
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mention_entities.add(entity)
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entities_by_mention[mention] = mention_entities
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for mention, entity_negs in incorrect_entries[article_id].items():
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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) + "_" + article_id + "_" + mention
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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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article_terms.add(mention)
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mention_entities = entities_by_mention.get(mention, set())
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mention_entities.add(entity)
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entities_by_mention[mention] = mention_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(article_terms))
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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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sentence_to_id = dict()
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for match_id, start, end in matches:
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span = article_doc[start:end]
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sent_text = span.sent
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sent_nr = sentence_to_id.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_to_id[sent_text] = sent_nr
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mention_entities = entities_by_mention[span.text]
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for entity in mention_entities:
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entities.add(entity)
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sentence_by_entity[entity] = sent_nr
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article_by_entity[entity] = article_id
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# remove entities that didn't have all data
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gold_by_entity = {k: v for k, v in gold_by_entity.items() if k in entities}
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desc_by_entity = {k: v for k, v in desc_by_entity.items() if k in entities}
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article_by_entity = {k: v for k, v in article_by_entity.items() if k in entities}
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text_by_article = {k: v for k, v in text_by_article.items() if k in article_by_entity.values()}
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sentence_by_entity = {k: v for k, v in sentence_by_entity.items() if k in entities}
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text_by_sentence = {k: v for k, v in text_by_sentence.items() if k in sentence_by_entity.values()}
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if to_print:
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print()
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print("Processed", cnt, "training articles, dev=" + str(dev))
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print()
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return list(entities), gold_by_entity, desc_by_entity, article_by_entity, text_by_article, sentence_by_entity, text_by_sentence
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