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implement loss function using dot product and prob estimate per candidate cluster

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svlandeg 2019-05-14 22:55:56 +02:00
parent 09ed446b20
commit 2713abc651
2 changed files with 103 additions and 102 deletions
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203
examples/pipeline/wiki_entity_linking/train_el.py
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@ -5,12 +5,14 @@ import os
import datetime
from os import listdir
from random import shuffle
import numpy as np
from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
from spacy._ml import SpacyVectors, create_default_optimizer, zero_init
from thinc.api import chain, flatten_add_lengths, with_getitem, clone
from thinc.neural.util import get_array_module
from thinc.v2v import Model, Softmax, Maxout, Affine, ReLu
from thinc.t2v import Pooling, sum_pool, mean_pool
from thinc.t2t import ParametricAttention
@ -23,6 +25,11 @@ from spacy.tokens import Doc
class EL_Model():
INPUT_DIM = 300
OUTPUT_DIM = 5 # 96
PRINT_LOSS = True
PRINT_F = True
labels = ["MATCH", "NOMATCH"]
name = "entity_linker"
@ -31,8 +38,8 @@ class EL_Model():
self.nlp = nlp
self.kb = kb
self.entity_encoder = self._simple_encoder(in_width=300, out_width=96)
self.article_encoder = self._simple_encoder(in_width=300, out_width=96)
self.entity_encoder = self._simple_encoder(in_width=self.INPUT_DIM, out_width=self.OUTPUT_DIM)
self.article_encoder = self._simple_encoder(in_width=self.INPUT_DIM, out_width=self.OUTPUT_DIM)
def train_model(self, training_dir, entity_descr_output, trainlimit=None, devlimit=None, to_print=True):
Doc.set_extension("entity_id", default=None)
@ -64,17 +71,20 @@ class EL_Model():
instance_count = 0
for article_id, inst_cluster_set in train_instances.items():
print("article", article_id)
article_doc = train_doc[article_id]
pos_ex_list = list()
neg_exs_list = list()
for inst_cluster in inst_cluster_set:
print("inst_cluster", inst_cluster)
instance_count += 1
pos_ex_list.append(train_pos.get(inst_cluster))
neg_exs_list.append(train_neg.get(inst_cluster, []))
self.update(article_doc, pos_ex_list, neg_exs_list, losses=losses)
p, r, fscore = self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc)
print(round(fscore, 1))
if self.PRINT_F:
print(round(fscore, 1))
if to_print:
print("Trained on", instance_count, "instance clusters")
@ -102,7 +112,7 @@ class EL_Model():
examples.append(pos_ex)
shuffle(examples)
best_entity, lowest_mse = self._predict(examples, article_doc)
best_entity, highest_prob = self._predict(examples, article_doc)
predictions.append(ex_to_id[best_entity])
golds.append(ex_to_id[pos_ex])
@ -113,17 +123,21 @@ class EL_Model():
def _predict(self, entities, article_doc):
doc_encoding = self.article_encoder([article_doc])
lowest_mse = None
highest_prob = None
best_entity = None
entity_to_vector = dict()
for entity in entities:
entity_encoding = self.entity_encoder([entity])
mse, _ = self._calculate_similarity(doc_encoding, entity_encoding)
if not best_entity or mse < lowest_mse:
lowest_mse = mse
entity_to_vector[entity] = self.entity_encoder([entity])
for entity in entities:
entity_encoding = entity_to_vector[entity]
prob = self._calculate_probability(doc_encoding, entity_encoding, entity_to_vector.values())
if not best_entity or prob > highest_prob:
highest_prob = prob
best_entity = entity
return best_entity, lowest_mse
return best_entity, highest_prob
def _simple_encoder(self, in_width, out_width):
conv_depth = 1
@ -164,103 +178,56 @@ class EL_Model():
return sgd
def update(self, article_doc, true_entity_list, false_entities_list, drop=0., losses=None):
# TODO: one call only to begin_update ?
entity_diffs = None
doc_diffs = None
doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
for i, true_entity in enumerate(true_entity_list):
false_entities = false_entities_list[i]
for cnt in range(10):
#try:
false_vectors = list()
false_entities = false_entities_list[i]
if len(false_entities) > 0:
# TODO: batch per doc
doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
doc_encoding = doc_encoding[0]
print()
print(cnt)
print("doc", doc_encoding)
true_entity_encoding, true_entity_bp = self.entity_encoder.begin_update([true_entity], drop=drop)
# print("encoding dim", len(true_entity_encoding[0]))
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)
consensus_encoding = self._calculate_consensus(doc_encoding, true_entity_encoding)
true_entity_encoding, true_entity_bp = self.entity_encoder.begin_update([true_entity], drop=drop)
true_entity_encoding = true_entity_encoding[0]
doc_mse, doc_diff = self._calculate_similarity(doc_encoding, consensus_encoding)
all_vectors = [true_entity_encoding]
all_vectors.extend(false_vectors)
entity_mses = list()
# consensus_encoding = self._calculate_consensus(doc_encoding, true_entity_encoding)
true_mse, true_diffs = self._calculate_similarity(true_entity_encoding, consensus_encoding)
# print("true_mse", true_mse)
# print("true_diffs", true_diffs)
entity_mses.append(true_mse)
# true_exp = np.exp(true_entity_encoding.dot(consensus_encoding_t))
# print("true_exp", true_exp)
true_prob = self._calculate_probability(doc_encoding, true_entity_encoding, all_vectors)
print("true", true_prob, true_entity_encoding)
# false_exp_sum = 0
all_probs = [true_prob]
for false_vector in false_vectors:
false_prob = self._calculate_probability(doc_encoding, false_vector, all_vectors)
print("false", false_prob, false_vector)
all_probs.append(false_prob)
if doc_diffs is not None:
doc_diffs += doc_diff
entity_diffs += true_diffs
else:
doc_diffs = doc_diff
entity_diffs = true_diffs
loss = self._calculate_loss(true_prob, all_probs).astype(np.float32)
if self.PRINT_LOSS:
print("loss", round(loss, 5))
for false_entity in false_entities:
false_entity_encoding, false_entity_bp = self.entity_encoder.begin_update([false_entity], drop=drop)
false_mse, false_diffs = self._calculate_similarity(false_entity_encoding, consensus_encoding)
# print("false_mse", false_mse)
# false_exp = np.exp(false_entity_encoding.dot(consensus_encoding_t))
# print("false_exp", false_exp)
# print("false_diffs", false_diffs)
entity_mses.append(false_mse)
# if false_mse > true_mse:
# true_diffs = true_diffs - false_diffs ???
# false_exp_sum += false_exp
# prob = true_exp / false_exp_sum
# print("prob", prob)
entity_mses = sorted(entity_mses)
# mse_sum = sum(entity_mses)
# entity_probs = [1 - x/mse_sum for x in entity_mses]
# print("entity_mses", entity_mses)
# print("entity_probs", entity_probs)
true_index = entity_mses.index(true_mse)
# print("true index", true_index)
# print("true prob", entity_probs[true_index])
# print("training loss", true_mse)
# print()
# TODO: proper backpropagation taking ranking of elements into account ?
# TODO backpropagation also for negative examples
if doc_diffs is not None:
doc_diffs = doc_diffs / len(true_entity_list)
true_entity_bp(entity_diffs, sgd=self.sgd_entity)
article_bp(doc_diffs, sgd=self.sgd_article)
doc_gradient = self._calculate_doc_gradient(loss, doc_encoding, true_entity_encoding, false_vectors)
print("doc_gradient", doc_gradient)
article_bp([doc_gradient.astype(np.float32)], sgd=self.sgd_article)
#except Exception as e:
#pass
# TODO delete ?
def _simple_cnn_model(self, internal_dim):
nr_class = len(self.labels)
with Model.define_operators({">>": chain}):
model_entity = SpacyVectors >> flatten_add_lengths >> Pooling(mean_pool) # entity encoding
model_doc = SpacyVectors >> flatten_add_lengths >> Pooling(mean_pool) # doc encoding
output_layer = Softmax(nr_class, internal_dim*2)
model = (model_entity | model_doc) >> output_layer
# model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
return model
def predict(self, entity_doc, article_doc):
entity_encoding = self.entity_encoder(entity_doc)
doc_encoding = self.article_encoder(article_doc)
print("entity_encodings", len(entity_encoding), entity_encoding)
print("doc_encodings", len(doc_encoding), doc_encoding)
mse, diffs = self._calculate_similarity(entity_encoding, doc_encoding)
print("mse", mse)
return mse
# TODO: expand to more than 2 vectors
# TODO: FIX
def _calculate_consensus(self, vector1, vector2):
if len(vector1) != len(vector2):
raise ValueError("To calculate consenus, both vectors should be of equal length")
@ -268,17 +235,51 @@ class EL_Model():
avg = (vector2 + vector1) / 2
return avg
def _calculate_similarity(self, vector1, vector2):
def _calculate_probability(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")
diffs = (vector1 - vector2)
error_sum = (diffs ** 2).sum()
mean_square_error = error_sum / len(vector1)
return float(mean_square_error), diffs
vector1_t = vector1.transpose()
e = self._calculate_dot_exp(vector2, vector1_t)
e_sum = 0
for v in allvectors:
e_sum += self._calculate_dot_exp(v, vector1_t)
def _get_labels(self):
return tuple(self.labels)
return float(e / e_sum)
@staticmethod
def _calculate_loss(true_prob, all_probs):
""" all_probs should include true_prob ! """
return -1 * np.log(true_prob / sum(all_probs))
@staticmethod
def _calculate_doc_gradient(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)
max_false = max(x[i] for x in false_vectors)
if true_vector[i] > max_false:
if doc_vector[i] > 0:
gradient[i] = 0
else:
gradient[i] = -loss
elif true_vector[i] < min_false:
if doc_vector[i] > 0:
gradient[i] = loss
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
return gradient
@staticmethod
def _calculate_dot_exp(vector1, vector2_transposed):
e = np.exp(vector1.dot(vector2_transposed))
return e
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)

2
examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
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@ -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=50, devlimit=50)
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1, devlimit=5)
print()
# STEP 7: apply the EL algorithm on the dev dataset