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first stab at model - not functional yet
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svlandeg
parent
9f33732b96
commit
c6ca8649d7
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@ -6,53 +6,168 @@ import datetime
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from os import listdir
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from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
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from examples.pipeline.wiki_entity_linking import wikidata_processor as wd
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from spacy._ml import SpacyVectors, create_default_optimizer, zero_init
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from thinc.api import chain
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from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
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from thinc.api import flatten_add_lengths
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from thinc.t2v import Pooling, sum_pool, mean_pool
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from thinc.t2t import ExtractWindow, ParametricAttention
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from thinc.misc import Residual
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""" TODO: this code needs to be implemented in pipes.pyx"""
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def train_model(kb, nlp, training_dir, entity_descr_output, limit=None):
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class EL_Model():
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labels = ["MATCH", "NOMATCH"]
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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.entity_encoder = self._simple_encoder(width=300)
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self.article_encoder = self._simple_encoder(width=300)
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def train_model(self, training_dir, entity_descr_output, limit=None, to_print=True):
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instances, gold_vectors, entity_descriptions, doc_by_article = self._get_training_data(training_dir,
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entity_descr_output,
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limit, to_print)
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if to_print:
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print("Training on", len(gold_vectors), "instances")
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print(" - pos:", len([x for x in gold_vectors if x]), "instances")
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print(" - pos:", len([x for x in gold_vectors if not x]), "instances")
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print()
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self.sgd_entity = self.begin_training(self.entity_encoder)
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self.sgd_article = self.begin_training(self.article_encoder)
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losses = {}
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for inst, label, entity_descr in zip(instances, gold_vectors, entity_descriptions):
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article = inst.split(sep="_")[0]
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entity_id = inst.split(sep="_")[1]
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article_doc = doc_by_article[article]
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self.update(article_doc, entity_descr, label, losses=losses)
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def _simple_encoder(self, width):
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with Model.define_operators({">>": chain}):
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encoder = SpacyVectors \
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>> flatten_add_lengths \
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>> ParametricAttention(width)\
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>> Pooling(sum_pool) \
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>> Residual(zero_init(Maxout(width, width)))
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return encoder
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def begin_training(self, model):
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# TODO ? link_vectors_to_models(self.vocab)
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sgd = create_default_optimizer(model.ops)
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return sgd
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def update(self, article_doc, entity_descr, label, drop=0., losses=None):
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entity_encoding, entity_bp = self.entity_encoder.begin_update([entity_descr], drop=drop)
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doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
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# print("entity/article output dim", len(entity_encoding[0]), len(doc_encoding[0]))
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mse, diffs = self._calculate_similarity(entity_encoding, doc_encoding)
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# print()
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# TODO: proper backpropagation taking ranking of elements into account ?
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# TODO backpropagation also for negative examples
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if label:
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entity_bp(diffs, sgd=self.sgd_entity)
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article_bp(diffs, sgd=self.sgd_article)
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print(mse)
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# TODO delete ?
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def _simple_cnn_model(self, internal_dim):
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nr_class = len(self.labels)
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with Model.define_operators({">>": chain}):
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model_entity = SpacyVectors >> flatten_add_lengths >> Pooling(mean_pool) # entity encoding
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model_doc = SpacyVectors >> flatten_add_lengths >> Pooling(mean_pool) # doc encoding
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output_layer = Softmax(nr_class, internal_dim*2)
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model = (model_entity | model_doc) >> output_layer
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# model.tok2vec = chain(tok2vec, flatten)
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model.nO = nr_class
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return model
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def predict(self, entity_doc, article_doc):
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entity_encoding = self.entity_encoder(entity_doc)
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doc_encoding = self.article_encoder(article_doc)
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print("entity_encodings", len(entity_encoding), entity_encoding)
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print("doc_encodings", len(doc_encoding), doc_encoding)
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mse, diffs = self._calculate_similarity(entity_encoding, doc_encoding)
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print("mse", mse)
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return mse
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def _calculate_similarity(self, vector1, vector2):
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if len(vector1) != len(vector2):
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raise ValueError("To calculate similarity, both vectors should be of equal length")
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diffs = (vector2 - vector1)
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error_sum = (diffs ** 2).sum(axis=1)
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mean_square_error = error_sum / len(vector1)
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return float(mean_square_error), diffs
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def _get_labels(self):
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return tuple(self.labels)
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def _get_training_data(self, training_dir, entity_descr_output, 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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entities = kb.get_entity_strings()
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id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
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instances = list()
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entity_descriptions = list()
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local_vectors = list() # TODO: local vectors
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gold_vectors = list()
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doc_by_article = dict()
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cnt = 0
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for f in listdir(training_dir):
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if not limit or cnt < limit:
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if not run_el.is_dev(f):
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article_id = f.replace(".txt", "")
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if cnt % 500 == 0:
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if cnt % 500 == 0 and to_print:
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print(datetime.datetime.now(), "processed", cnt, "files in the dev dataset")
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cnt += 1
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if article_id not in doc_by_article:
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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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print()
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doc = nlp(text)
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doc_vector = doc.vector
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print("FILE", f, len(doc_vector), "D vector")
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doc = self.nlp(text)
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doc_by_article[article_id] = doc
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for mention_pos, 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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doc_descr = nlp(descr)
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descr_vector = doc_descr.vector
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print("GOLD POS", mention_pos, entity_pos, len(descr_vector), "D vector")
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instances.append(article_id + "_" + entity_pos)
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doc = self.nlp(descr)
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entity_descriptions.append(doc)
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gold_vectors.append(True)
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for mention_neg, 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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doc_descr = nlp(descr)
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descr_vector = doc_descr.vector
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print("GOLD NEG", mention_neg, entity_neg, len(descr_vector), "D vector")
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instances.append(article_id + "_" + entity_neg)
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doc = self.nlp(descr)
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entity_descriptions.append(doc)
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gold_vectors.append(False)
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if to_print:
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print()
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print("Processed", cnt, "dev articles")
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print()
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return instances, gold_vectors, entity_descriptions, doc_by_article
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@ -1,7 +1,8 @@
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# coding: utf-8
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from __future__ import unicode_literals
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from examples.pipeline.wiki_entity_linking import wikipedia_processor as wp, kb_creator, training_set_creator, run_el, train_el
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from examples.pipeline.wiki_entity_linking import wikipedia_processor as wp, kb_creator, training_set_creator, run_el
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from examples.pipeline.wiki_entity_linking.train_el import EL_Model
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import spacy
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from spacy.vocab import Vocab
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@ -31,17 +32,17 @@ if __name__ == "__main__":
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# one-time methods to create KB and write to file
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to_create_prior_probs = False
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to_create_entity_counts = False
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to_create_kb = True
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to_create_kb = False
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# read KB back in from file
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to_read_kb = True
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to_test_kb = True
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to_test_kb = False
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# create training dataset
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create_wp_training = False
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# run training
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run_training = False
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run_training = True
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# apply named entity linking to the dev dataset
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apply_to_dev = False
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@ -105,16 +106,17 @@ if __name__ == "__main__":
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print("STEP 5: create training dataset", datetime.datetime.now())
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training_set_creator.create_training(kb=my_kb, entity_def_input=ENTITY_DEFS, training_output=TRAINING_DIR)
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# STEP 7: apply the EL algorithm on the training dataset
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# STEP 6: apply the EL algorithm on the training dataset
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if run_training:
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print("STEP 6: training ", datetime.datetime.now())
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my_nlp = spacy.load('en_core_web_sm')
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train_el.train_model(kb=my_kb, nlp=my_nlp, training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, limit=5)
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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, limit=50)
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print()
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# STEP 8: apply the EL algorithm on the dev dataset
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# STEP 7: apply the EL algorithm on the dev dataset
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if apply_to_dev:
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my_nlp = spacy.load('en_core_web_sm')
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my_nlp = spacy.load('en_core_web_md')
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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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