mirror of https://github.com/explosion/spaCy.git
various tests, architectures and experiments
This commit is contained in:
svlandeg
parent
9ffe5437ae
commit
b5470f3d75
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@ -6,32 +6,40 @@ import datetime
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from os import listdir
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from random import shuffle
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import numpy as np
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import random
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from thinc.neural._classes.convolution import ExtractWindow
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from thinc.neural._classes.feature_extracter import FeatureExtracter
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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
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from spacy._ml import SpacyVectors, create_default_optimizer, zero_init, logistic
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from thinc.api import chain, flatten_add_lengths, with_getitem, clone
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from thinc.api import chain, concatenate, flatten_add_lengths, with_getitem, clone, with_flatten
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from thinc.neural.util import get_array_module
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from thinc.v2v import Model, Softmax, Maxout, Affine, ReLu
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from thinc.t2v import Pooling, sum_pool, mean_pool
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from thinc.t2v import Pooling, sum_pool, mean_pool, max_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.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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class EL_Model:
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INPUT_DIM = 300
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OUTPUT_DIM = 96
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PRINT_LOSS = False
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PRINT_LOSS = True
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PRINT_F = True
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EPS = 0.0000000005
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CUTOFF = 0.5
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INPUT_DIM = 300
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ENTITY_WIDTH = 64
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ARTICLE_WIDTH = 64
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HIDDEN_1_WIDTH = 256
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HIDDEN_2_WIDTH = 64
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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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@ -39,58 +47,102 @@ class EL_Model():
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self.nlp = nlp
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self.kb = kb
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self.entity_encoder = self._simple_encoder(in_width=self.INPUT_DIM, out_width=self.OUTPUT_DIM)
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self.article_encoder = self._simple_encoder(in_width=self.INPUT_DIM, out_width=self.OUTPUT_DIM)
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self._build_cnn(hidden_entity_width=self.ENTITY_WIDTH, hidden_article_width=self.ARTICLE_WIDTH)
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# self.entity_encoder = self._simple_encoder(in_width=self.INPUT_DIM, out_width=self.OUTPUT_DIM)
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# self.article_encoder = self._simple_encoder(in_width=self.INPUT_DIM, out_width=self.OUTPUT_DIM)
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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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Doc.set_extension("entity_id", default=None)
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train_instances, train_pos, train_neg, train_doc = self._get_training_data(training_dir,
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entity_descr_output,
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False,
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trainlimit,
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to_print)
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to_print=False)
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dev_instances, dev_pos, dev_neg, dev_doc = self._get_training_data(training_dir,
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entity_descr_output,
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True,
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devlimit,
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to_print)
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to_print=False)
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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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self._begin_training()
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if self.PRINT_F:
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_, _, f_avg_train = -3.42, -3.42, -3.42 # self._test_dev(train_instances, train_pos, train_neg, train_doc, avg=True)
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_, _, f_nonavg_train = self._test_dev(train_instances, train_pos, train_neg, train_doc, avg=False)
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_, _, f_random_train = self._test_dev(train_instances, train_pos, train_neg, train_doc, calc_random=True)
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_, _, f_avg_dev = -3.42, -3.42, -3.42 # self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc, avg=True)
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_, _, f_nonavg_dev = self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc, avg=False)
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_, _, f_random_dev = self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc, calc_random=True)
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print("random F train", round(f_random_train, 1))
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print("random F dev", round(f_random_dev, 1))
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print()
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print("avg/nonavg F train", round(f_avg_train, 1), round(f_nonavg_train, 1))
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print("avg/nonavg F dev", round(f_avg_dev, 1), round(f_nonavg_dev, 1))
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print()
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instance_pos_count = 0
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instance_neg_count = 0
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if to_print:
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print("Training on", len(train_instances.values()), "articles")
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print("Dev test on", len(dev_instances.values()), "articles")
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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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# for article_id, inst_cluster_set in train_instances.items():
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# article_doc = train_doc[article_id]
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# print("training on", article_id, inst_cluster_set)
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# pos_ex_list = list()
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# neg_exs_list = list()
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# for inst_cluster in inst_cluster_set:
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# instance_count += 1
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# pos_ex_list.append(train_pos.get(inst_cluster))
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# neg_exs_list.append(train_neg.get(inst_cluster, []))
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self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc)
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losses = {}
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instance_count = 0
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#self.update(article_doc, pos_ex_list, neg_exs_list)
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article_docs = list()
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entities = list()
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golds = list()
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for article_id, inst_cluster_set in train_instances.items():
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# print("article", article_id)
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article_doc = train_doc[article_id]
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pos_ex_list = list()
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neg_exs_list = list()
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for inst_cluster in inst_cluster_set:
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# print("inst_cluster", inst_cluster)
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instance_count += 1
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pos_ex_list.append(train_pos.get(inst_cluster))
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neg_exs_list.append(train_neg.get(inst_cluster, []))
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article_docs.append(train_doc[article_id])
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entities.append(train_pos.get(inst_cluster))
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golds.append(float(1.0))
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instance_pos_count += 1
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for neg_entity in train_neg.get(inst_cluster, []):
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article_docs.append(train_doc[article_id])
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entities.append(neg_entity)
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golds.append(float(0.0))
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instance_neg_count += 1
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self.update(article_doc, pos_ex_list, neg_exs_list, losses=losses)
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p, r, fscore = self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc)
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for x in range(10):
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print("Updating", x)
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self.update(article_docs=article_docs, entities=entities, golds=golds)
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# eval again
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if self.PRINT_F:
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print(round(fscore, 1))
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_, _, f_avg_train = -3.42, -3.42, -3.42 # self._test_dev(train_instances, train_pos, train_neg, train_doc, avg=True)
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_, _, f_nonavg_train = self._test_dev(train_instances, train_pos, train_neg, train_doc, avg=False)
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_, _, f_avg_dev = -3.42, -3.42, -3.42 # self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc, avg=True)
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_, _, f_nonavg_dev = self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc, avg=False)
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print("avg/nonavg F train", round(f_avg_train, 1), round(f_nonavg_train, 1))
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print("avg/nonavg F dev", round(f_avg_dev, 1), round(f_nonavg_dev, 1))
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print()
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if to_print:
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print("Trained on", instance_count, "instance clusters")
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print("Trained on", instance_pos_count, "/", instance_neg_count, "instances pos/neg")
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def _test_dev(self, dev_instances, dev_pos, dev_neg, dev_doc):
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def _test_dev_depr(self, dev_instances, dev_pos, dev_neg, dev_doc, avg=False, calc_random=False):
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predictions = list()
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golds = list()
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@ -113,15 +165,53 @@ class EL_Model():
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examples.append(pos_ex)
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shuffle(examples)
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best_entity, highest_prob = self._predict(examples, article_doc)
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best_entity, highest_prob = self._predict(examples, article_doc, avg)
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if calc_random:
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best_entity, highest_prob = self._predict_random(examples)
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predictions.append(ex_to_id[best_entity])
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golds.append(ex_to_id[pos_ex])
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# TODO: use lowest_mse and combine with prior probability
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p, r, F = run_el.evaluate(predictions, golds, to_print=False)
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return p, r, F
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p, r, f = run_el.evaluate(predictions, golds, to_print=False)
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return p, r, f
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def _predict(self, entities, article_doc):
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def _test_dev(self, dev_instances, dev_pos, dev_neg, dev_doc, avg=False, calc_random=False):
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predictions = list()
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golds = list()
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for article_id, inst_cluster_set in dev_instances.items():
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for inst_cluster in inst_cluster_set:
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pos_ex = dev_pos.get(inst_cluster)
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neg_exs = dev_neg.get(inst_cluster, [])
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article = inst_cluster.split(sep="_")[0]
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entity_id = inst_cluster.split(sep="_")[1]
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article_doc = dev_doc[article]
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if calc_random:
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prediction = self._predict_random(entity=pos_ex)
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else:
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prediction = self._predict(article_doc=article_doc, entity=pos_ex, avg=avg)
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predictions.append(prediction)
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golds.append(float(1.0))
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for neg_ex in neg_exs:
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if calc_random:
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prediction = self._predict_random(entity=neg_ex)
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else:
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prediction = self._predict(article_doc=article_doc, entity=neg_ex, avg=avg)
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predictions.append(prediction)
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golds.append(float(0.0))
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# TODO: use lowest_mse and combine with prior probability
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p, r, f = run_el.evaluate(predictions, golds, to_print=False)
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return p, r, f
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def _predict_depr(self, entities, article_doc, avg=False):
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if avg:
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with self.article_encoder.use_params(self.sgd_article.averages):
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doc_encoding = self.article_encoder([article_doc])
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else:
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doc_encoding = self.article_encoder([article_doc])
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highest_prob = None
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@ -129,6 +219,10 @@ class EL_Model():
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entity_to_vector = dict()
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for entity in entities:
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if avg:
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with self.entity_encoder.use_params(self.sgd_entity.averages):
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entity_to_vector[entity] = self.entity_encoder([entity])
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else:
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entity_to_vector[entity] = self.entity_encoder([entity])
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for entity in entities:
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@ -140,7 +234,97 @@ class EL_Model():
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return best_entity, highest_prob
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def _simple_encoder(self, in_width, out_width):
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def _predict(self, article_doc, entity, avg=False, apply_threshold=True):
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if avg:
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with self.sgd.use_params(self.model.averages):
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doc_encoding = self.article_encoder([article_doc])
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entity_encoding = self.entity_encoder([entity])
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return self.model(np.append(entity_encoding, doc_encoding)) # TODO list
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doc_encoding = self.article_encoder([article_doc])[0]
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entity_encoding = self.entity_encoder([entity])[0]
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concat_encoding = list(entity_encoding) + list(doc_encoding)
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np_array = np.asarray([concat_encoding])
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prediction = self.model(np_array)
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if not apply_threshold:
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return float(prediction)
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if prediction > self.CUTOFF:
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return float(1.0)
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return float(0.0)
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def _predict_random_depr(self, entities):
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highest_prob = 1
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best_entity = random.choice(entities)
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return best_entity, highest_prob
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def _predict_random(self, entity, apply_threshold=True):
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r = random.uniform(0, 1)
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if not apply_threshold:
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return r
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if r > self.CUTOFF:
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return float(1.0)
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return float(0.0)
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def _build_cnn(self, hidden_entity_width, hidden_article_width):
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with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
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self.entity_encoder = self._encoder(in_width=self.INPUT_DIM, hidden_width=hidden_entity_width) # entity encoding
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self.article_encoder = self._encoder(in_width=self.INPUT_DIM, hidden_width=hidden_article_width) # doc encoding
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hidden_input_with = hidden_entity_width + hidden_article_width
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hidden_output_with = self.HIDDEN_1_WIDTH
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convolution_2 = Residual((ExtractWindow(nW=1) >> LN(Maxout(hidden_output_with, hidden_output_with * 3))))
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# self.entity_encoder | self.article_encoder \
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# self.model = with_flatten(LN(Maxout(hidden_with, hidden_with)) >> convolution_2 ** 2, pad=2) \
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# >> flatten_add_lengths \
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# >> ParametricAttention(hidden_with) \
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# >> Pooling(sum_pool) \
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# >> Softmax(nr_class, nr_class)
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self.model = Affine(hidden_output_with, hidden_input_with) \
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>> LN(Maxout(hidden_output_with, hidden_output_with)) \
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>> convolution_2 \
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>> Affine(self.HIDDEN_2_WIDTH, hidden_output_with) \
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>> Affine(1, self.HIDDEN_2_WIDTH) \
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>> logistic
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# >> with_flatten(LN(Maxout(hidden_output_with, hidden_output_with)) >> convolution_2 ** 2, pad=2)
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# >> convolution_2 \
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# >> flatten_add_lengths
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# >> ParametricAttention(hidden_output_with) \
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# >> Pooling(max_pool) \
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# >> Softmax(nr_class, nr_class)
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# self.model.nO = nr_class
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@staticmethod
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def _encoder(in_width, hidden_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(in_width)\
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>> Pooling(mean_pool) \
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>> Residual(zero_init(Maxout(in_width, in_width))) \
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>> zero_init(Affine(hidden_width, in_width, drop_factor=0.0))
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return encoder
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def begin_training_depr(self, model):
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# TODO ? link_vectors_to_models(self.vocab) depr?
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sgd = create_default_optimizer(model.ops)
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return sgd
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def _begin_training(self):
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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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self.sgd = create_default_optimizer(self.model.ops)
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# TODO: deprecated ?
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def _simple_encoder_depr(self, in_width, out_width):
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hidden_with = 128
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conv_depth = 1
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cnn_maxout_pieces = 3
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with Model.define_operators({">>": chain, "**": clone}):
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@ -150,21 +334,56 @@ class EL_Model():
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# >> Pooling(mean_pool) \
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# >> Residual(zero_init(Maxout(in_width, in_width))) \
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# >> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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encoder = SpacyVectors \
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>> flatten_add_lengths \
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>> with_getitem(0, Affine(in_width, in_width)) \
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>> ParametricAttention(in_width) \
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>> Pooling(sum_pool) \
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>> Residual(ReLu(in_width, in_width)) ** conv_depth \
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>> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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# encoder = SpacyVectors \
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# >> flatten_add_lengths \
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# >> with_getitem(0, Affine(in_width, in_width)) \
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# >> ParametricAttention(in_width) \
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# >> Pooling(sum_pool) \
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# >> Residual(ReLu(in_width, in_width)) ** conv_depth \
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# >> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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# encoder = SpacyVectors \
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# >> flatten_add_lengths \
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# >> ParametricAttention(in_width)\
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# >> Pooling(sum_pool) \
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# >> Residual(zero_init(Maxout(in_width, in_width))) \
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# >> zero_init(Affine(out_width, in_width, drop_factor=0.0))
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# >> zero_init(Affine(nr_class, width, drop_factor=0.0))
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# >> logistic
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# convolution = Residual(
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# ExtractWindow(nW=1)
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# >> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
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# )
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#convolution = Residual(ExtractWindow(nW=1)
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# >> LN(Maxout(in_width, in_width * 3, pieces=cnn_maxout_pieces))
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#)
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#encoder = SpacyVectors >> with_flatten(
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# embed >> convolution ** conv_depth, pad=conv_depth
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#)
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# static_vectors = SpacyVectors >> with_flatten(
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# Affine(in_width, in_width)
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#)
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convolution_2 = Residual((ExtractWindow(nW=1) >> LN(Maxout(hidden_with, hidden_with * 3))))
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encoder = SpacyVectors >> with_flatten(LN(Maxout(hidden_with, in_width)) >> convolution_2 ** 2, pad = 2) \
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>> flatten_add_lengths \
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>> ParametricAttention(hidden_with) \
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>> Pooling(sum_pool) \
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>> Residual(zero_init(Maxout(hidden_with, hidden_with))) \
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>> zero_init(Affine(out_width, hidden_with, drop_factor=0.0)) \
|
||||
>> logistic
|
||||
|
||||
# convolution = Residual(ExtractWindow(nW=1) >> ReLu(in_width, in_width*3))
|
||||
|
||||
# encoder = static_vectors # >> with_flatten(
|
||||
# ReLu(in_width, in_width)
|
||||
# >> convolution ** conv_depth, pad=conv_depth) \
|
||||
# >> Affine(out_width, in_width, drop_factor=0.0)
|
||||
|
||||
# encoder = SpacyVectors >> with_flatten(
|
||||
# LN(Maxout(in_width, in_width))
|
||||
# >> Residual((ExtractWindow(nW=1) >> LN(Maxout(in_width, in_width * 3, pieces=cnn_maxout_pieces)))) ** conv_depth,
|
||||
# pad=conv_depth,
|
||||
#) >> zero_init(Affine(out_width, in_width, drop_factor=0.0))
|
||||
|
||||
# embed = SpacyVectors >> LN(Maxout(width, width, pieces=3))
|
||||
|
||||
|
|
@ -173,75 +392,91 @@ class EL_Model():
|
|||
|
||||
return encoder
|
||||
|
||||
def begin_training(self, model):
|
||||
# TODO ? link_vectors_to_models(self.vocab)
|
||||
sgd = create_default_optimizer(model.ops)
|
||||
return sgd
|
||||
|
||||
def update(self, article_doc, true_entity_list, false_entities_list, drop=0., losses=None):
|
||||
def update_depr(self, article_doc, true_entity_list, false_entities_list, drop=0., losses=None):
|
||||
doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
|
||||
doc_encoding = doc_encoding[0]
|
||||
# print()
|
||||
# print("doc", doc_encoding)
|
||||
|
||||
for i, true_entity in enumerate(true_entity_list):
|
||||
try:
|
||||
false_vectors = list()
|
||||
false_entities = false_entities_list[i]
|
||||
if len(false_entities) > 0:
|
||||
# TODO: batch per doc
|
||||
|
||||
for false_entity in false_entities:
|
||||
# TODO: one call only to begin_update ?
|
||||
false_entity_encoding, false_entity_bp = self.entity_encoder.begin_update([false_entity], drop=drop)
|
||||
false_entity_encoding = false_entity_encoding[0]
|
||||
false_vectors.append(false_entity_encoding)
|
||||
all_entities = [true_entity]
|
||||
all_entities.extend(false_entities)
|
||||
|
||||
true_entity_encoding, true_entity_bp = self.entity_encoder.begin_update([true_entity], drop=drop)
|
||||
true_entity_encoding = true_entity_encoding[0]
|
||||
# true_gradient = self._calculate_true_gradient(doc_encoding, true_entity_encoding)
|
||||
entity_encodings, entity_bp = self.entity_encoder.begin_update(all_entities, drop=drop)
|
||||
true_entity_encoding = entity_encodings[0]
|
||||
false_entity_encodings = entity_encodings[1:]
|
||||
|
||||
all_vectors = [true_entity_encoding]
|
||||
all_vectors.extend(false_vectors)
|
||||
all_vectors.extend(false_entity_encodings)
|
||||
|
||||
# consensus_encoding = self._calculate_consensus(doc_encoding, true_entity_encoding)
|
||||
|
||||
true_prob = self._calculate_probability(doc_encoding, true_entity_encoding, all_vectors)
|
||||
# print("true", true_prob, true_entity_encoding)
|
||||
# print("true gradient", true_gradient)
|
||||
# print()
|
||||
|
||||
all_probs = [true_prob]
|
||||
for false_vector in false_vectors:
|
||||
for false_vector in false_entity_encodings:
|
||||
false_prob = self._calculate_probability(doc_encoding, false_vector, all_vectors)
|
||||
# print("false", false_prob, false_vector)
|
||||
# print("false gradient", false_gradient)
|
||||
# print()
|
||||
all_probs.append(false_prob)
|
||||
|
||||
loss = self._calculate_loss(true_prob, all_probs).astype(np.float32)
|
||||
if self.PRINT_LOSS:
|
||||
print(round(loss, 5))
|
||||
print("loss train", round(loss, 5))
|
||||
|
||||
#doc_gradient = self._calculate_doc_gradient(loss, doc_encoding, true_entity_encoding, false_vectors)
|
||||
entity_gradient = self._calculate_entity_gradient(doc_encoding, true_entity_encoding, false_vectors)
|
||||
# print("entity_gradient", entity_gradient)
|
||||
# for false_vector in false_vectors:
|
||||
# false_gradient = -1 * self._calculate_entity_gradient(loss, doc_encoding, false_vector, false_vectors)
|
||||
# print("false gradient", false_gradient)
|
||||
|
||||
# doc_gradient = self._calculate_doc_gradient(loss, doc_encoding, true_entity_encoding, false_entity_encodings)
|
||||
true_gradient, doc_gradient = self._calculate_entity_gradient(loss, doc_encoding, true_entity_encoding, false_entity_encodings)
|
||||
# print("true_gradient", true_gradient)
|
||||
# print("doc_gradient", doc_gradient)
|
||||
# article_bp([doc_gradient.astype(np.float32)], sgd=self.sgd_article)
|
||||
true_entity_bp([entity_gradient.astype(np.float32)], sgd=self.sgd_entity)
|
||||
article_bp([doc_gradient.astype(np.float32)], sgd=self.sgd_article)
|
||||
entity_bp([true_gradient.astype(np.float32)], sgd=self.sgd_entity)
|
||||
#true_entity_bp([true_gradient.astype(np.float32)], sgd=self.sgd_entity)
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
def update(self, article_docs, entities, golds, drop=0.):
|
||||
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))]
|
||||
|
||||
# TODO: FIX
|
||||
def _calculate_consensus(self, vector1, vector2):
|
||||
if len(vector1) != len(vector2):
|
||||
raise ValueError("To calculate consensus, both vectors should be of equal length")
|
||||
predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=drop)
|
||||
|
||||
avg = (vector2 + vector1) / 2
|
||||
return avg
|
||||
predictions = self.model.ops.flatten(predictions)
|
||||
golds = self.model.ops.asarray(golds)
|
||||
|
||||
def _calculate_probability(self, vector1, vector2, allvectors):
|
||||
# print("predictions", predictions)
|
||||
# print("golds", golds)
|
||||
|
||||
d_scores = (predictions - golds) # / predictions.shape[0]
|
||||
# print("d_scores (1)", d_scores)
|
||||
|
||||
loss = (d_scores ** 2).sum()
|
||||
|
||||
if self.PRINT_LOSS:
|
||||
print("loss train", round(loss, 5))
|
||||
|
||||
d_scores = d_scores.reshape((-1, 1))
|
||||
d_scores = d_scores.astype(np.float32)
|
||||
# print("d_scores (2)", d_scores)
|
||||
|
||||
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 _calculate_probability_depr(self, vector1, vector2, allvectors):
|
||||
""" Make sure that vector2 is included in allvectors """
|
||||
if len(vector1) != len(vector2):
|
||||
raise ValueError("To calculate similarity, both vectors should be of equal length")
|
||||
|
|
@ -254,12 +489,12 @@ class EL_Model():
|
|||
|
||||
return float(e / (self.EPS + e_sum))
|
||||
|
||||
def _calculate_loss(self, true_prob, all_probs):
|
||||
def _calculate_loss_depr(self, true_prob, all_probs):
|
||||
""" all_probs should include true_prob ! """
|
||||
return -1 * np.log((self.EPS + true_prob) / (self.EPS + sum(all_probs)))
|
||||
|
||||
@staticmethod
|
||||
def _calculate_doc_gradient(loss, doc_vector, true_vector, false_vectors):
|
||||
def _calculate_doc_gradient_depr(loss, doc_vector, true_vector, false_vectors):
|
||||
gradient = np.zeros(len(doc_vector))
|
||||
for i in range(len(doc_vector)):
|
||||
min_false = min(x[i] for x in false_vectors)
|
||||
|
|
@ -276,21 +511,25 @@ class EL_Model():
|
|||
if doc_vector[i] < 0:
|
||||
gradient[i] = 0
|
||||
else:
|
||||
target = 0 # non-distinctive vector positions should convert to 0
|
||||
gradient[i] = doc_vector[i] - target
|
||||
# non-distinctive vector positions should converge to 0
|
||||
gradient[i] = doc_vector[i]
|
||||
|
||||
return gradient
|
||||
|
||||
def _calculate_true_gradient(self, doc_vector, entity_vector):
|
||||
# TODO: delete ? try again ?
|
||||
def depr__calculate_true_gradient(self, doc_vector, entity_vector):
|
||||
# sum_entity_vector = sum(entity_vector)
|
||||
# gradient = [-sum_entity_vector/(self.EPS + np.exp(doc_vector[i] * entity_vector[i])) for i in range(len(doc_vector))]
|
||||
gradient = [1 / (self.EPS + np.exp(doc_vector[i] * entity_vector[i])) for i in range(len(doc_vector))]
|
||||
return np.asarray(gradient)
|
||||
|
||||
def _calculate_entity_gradient(self, doc_vector, true_vector, false_vectors):
|
||||
entity_gradient = list()
|
||||
prob_true = list()
|
||||
false_prob_list = list()
|
||||
def _calculate_losses_vector_depr(self, doc_vector, true_vector, false_vectors):
|
||||
# prob_true = list()
|
||||
# prob_false_dict = dict()
|
||||
|
||||
true_losses = list()
|
||||
# false_losses_dict = dict()
|
||||
|
||||
for i in range(len(true_vector)):
|
||||
doc_i = np.asarray([doc_vector[i]])
|
||||
true_i = np.asarray([true_vector[i]])
|
||||
|
|
@ -299,32 +538,45 @@ class EL_Model():
|
|||
all_i.extend(falses_i)
|
||||
|
||||
prob_true_i = self._calculate_probability(doc_i, true_i, all_i)
|
||||
prob_true.append(prob_true_i)
|
||||
# prob_true.append(prob_true_i)
|
||||
|
||||
false_list = list()
|
||||
# false_list = list()
|
||||
all_probs_i = [prob_true_i]
|
||||
for false_vector in falses_i:
|
||||
false_prob_i = self._calculate_probability(doc_i, false_vector, all_i)
|
||||
all_probs_i.append(false_prob_i)
|
||||
false_list.append(false_prob_i)
|
||||
false_prob_list.append(false_list)
|
||||
for false_i in falses_i:
|
||||
prob_false_i = self._calculate_probability(doc_i, false_i, all_i)
|
||||
all_probs_i.append(prob_false_i)
|
||||
# false_list.append(prob_false_i)
|
||||
# prob_false_dict[i] = false_list
|
||||
|
||||
sign_loss_i = 1
|
||||
if doc_vector[i] * true_vector[i] < 0:
|
||||
sign_loss_i = -1
|
||||
true_loss_i = self._calculate_loss(prob_true_i, all_probs_i).astype(np.float32)
|
||||
if doc_vector[i] > 0:
|
||||
true_loss_i = -1 * true_loss_i
|
||||
true_losses.append(true_loss_i)
|
||||
|
||||
loss_i = sign_loss_i * self._calculate_loss(prob_true_i, all_probs_i).astype(np.float32)
|
||||
entity_gradient.append(loss_i)
|
||||
# print("prob_true", prob_true)
|
||||
# print("false_prob_list", false_prob_list)
|
||||
return np.asarray(entity_gradient)
|
||||
# false_loss_list = list()
|
||||
# for prob_false_i in false_list:
|
||||
# false_loss_i = self._calculate_loss(prob_false_i, all_probs_i).astype(np.float32)
|
||||
# false_loss_list.append(false_loss_i)
|
||||
# false_losses_dict[i] = false_loss_list
|
||||
|
||||
return true_losses # , false_losses_dict
|
||||
|
||||
def _calculate_entity_gradient_depr(self, loss, doc_vector, true_vector, false_vectors):
|
||||
true_losses = self._calculate_losses_vector(doc_vector, true_vector, false_vectors)
|
||||
|
||||
# renormalize the gradient so that the total sum of abs values does not exceed the actual loss
|
||||
loss_i = sum([abs(x) for x in true_losses]) # sum of absolute values
|
||||
entity_gradient = [(x/2) * (loss/loss_i) for x in true_losses]
|
||||
doc_gradient = [(x/2) * (loss/loss_i) for x in true_losses]
|
||||
|
||||
return np.asarray(entity_gradient), np.asarray(doc_gradient)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _calculate_dot_exp(vector1, vector2_transposed):
|
||||
def _calculate_dot_exp_depr(vector1, vector2_transposed):
|
||||
dot_product = vector1.dot(vector2_transposed)
|
||||
dot_product = min(50, dot_product)
|
||||
# dot_product = max(-10000, dot_product)
|
||||
dot_product = max(-10000, dot_product)
|
||||
# print("DOT", dot_product)
|
||||
e = np.exp(dot_product)
|
||||
# print("E", e)
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ if __name__ == "__main__":
|
|||
print("STEP 6: training ", datetime.datetime.now())
|
||||
my_nlp = spacy.load('en_core_web_md')
|
||||
trainer = EL_Model(kb=my_kb, nlp=my_nlp)
|
||||
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1500, devlimit=50)
|
||||
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1, devlimit=1)
|
||||
print()
|
||||
|
||||
# STEP 7: apply the EL algorithm on the dev dataset
|
||||
|
|
|
|||
Loading…
Reference in New Issue