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spaCy/examples/pipeline/wikidata_entity_linking.py
svlandeg 0d177c1146 clean up code, remove old code, move to bin
2019-06-18 13:20:40 +02:00

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# coding: utf-8
from __future__ import unicode_literals
import random
from spacy.util import minibatch, compounding
from bin.wiki_entity_linking import training_set_creator, kb_creator, wikipedia_processor as wp
from bin.wiki_entity_linking.kb_creator import DESC_WIDTH
import spacy
from spacy.kb import KnowledgeBase
import datetime
"""
Demonstrate how to build a knowledge base from WikiData and run an Entity Linking algorithm.
"""
PRIOR_PROB = 'C:/Users/Sofie/Documents/data/wikipedia/prior_prob.csv'
ENTITY_COUNTS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_freq.csv'
ENTITY_DEFS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_defs.csv'
ENTITY_DESCR = 'C:/Users/Sofie/Documents/data/wikipedia/entity_descriptions.csv'
KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb_1/kb'
NLP_1_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_1'
NLP_2_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_2'
TRAINING_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/training_data_nel/'
MAX_CANDIDATES = 10
MIN_ENTITY_FREQ = 20
MIN_PAIR_OCC = 5
EPOCHS = 10
DROPOUT = 0.1
LEARN_RATE = 0.005
L2 = 1e-6
def run_pipeline():
print("START", datetime.datetime.now())
print()
nlp_1 = spacy.load('en_core_web_lg')
nlp_2 = None
kb_2 = None
# one-time methods to create KB and write to file
to_create_prior_probs = False
to_create_entity_counts = False
to_create_kb = False
# read KB back in from file
to_read_kb = True
to_test_kb = False
# create training dataset
create_wp_training = False
# train the EL pipe
train_pipe = True
measure_performance = True
# test the EL pipe on a simple example
to_test_pipeline = True
# write the NLP object, read back in and test again
to_write_nlp = False
to_read_nlp = False
# STEP 1 : create prior probabilities from WP
# run only once !
if to_create_prior_probs:
print("STEP 1: to_create_prior_probs", datetime.datetime.now())
wp.read_wikipedia_prior_probs(prior_prob_output=PRIOR_PROB)
print()
# STEP 2 : deduce entity frequencies from WP
# run only once !
if to_create_entity_counts:
print("STEP 2: to_create_entity_counts", datetime.datetime.now())
wp.write_entity_counts(prior_prob_input=PRIOR_PROB, count_output=ENTITY_COUNTS, to_print=False)
print()
# STEP 3 : create KB and write to file
# run only once !
if to_create_kb:
print("STEP 3a: to_create_kb", datetime.datetime.now())
kb_1 = kb_creator.create_kb(nlp_1,
max_entities_per_alias=MAX_CANDIDATES,
min_entity_freq=MIN_ENTITY_FREQ,
min_occ=MIN_PAIR_OCC,
entity_def_output=ENTITY_DEFS,
entity_descr_output=ENTITY_DESCR,
count_input=ENTITY_COUNTS,
prior_prob_input=PRIOR_PROB,
to_print=False)
print("kb entities:", kb_1.get_size_entities())
print("kb aliases:", kb_1.get_size_aliases())
print()
print("STEP 3b: write KB and NLP", datetime.datetime.now())
kb_1.dump(KB_FILE)
nlp_1.to_disk(NLP_1_DIR)
print()
# STEP 4 : read KB back in from file
if to_read_kb:
print("STEP 4: to_read_kb", datetime.datetime.now())
nlp_2 = spacy.load(NLP_1_DIR)
kb_2 = KnowledgeBase(vocab=nlp_2.vocab, entity_vector_length=DESC_WIDTH)
kb_2.load_bulk(KB_FILE)
print("kb entities:", kb_2.get_size_entities())
print("kb aliases:", kb_2.get_size_aliases())
print()
# test KB
if to_test_kb:
check_kb(kb_2)
print()
# STEP 5: create a training dataset from WP
if create_wp_training:
print("STEP 5: create training dataset", datetime.datetime.now())
training_set_creator.create_training(entity_def_input=ENTITY_DEFS, training_output=TRAINING_DIR)
# STEP 6: create and train the entity linking pipe
el_pipe = nlp_2.create_pipe(name='entity_linker', config={})
el_pipe.set_kb(kb_2)
nlp_2.add_pipe(el_pipe, last=True)
other_pipes = [pipe for pipe in nlp_2.pipe_names if pipe != "entity_linker"]
with nlp_2.disable_pipes(*other_pipes): # only train Entity Linking
optimizer = nlp_2.begin_training()
optimizer.learn_rate = LEARN_RATE
optimizer.L2 = L2
if train_pipe:
print("STEP 6: training Entity Linking pipe", datetime.datetime.now())
train_limit = 25000
dev_limit = 5000
train_data = training_set_creator.read_training(nlp=nlp_2,
training_dir=TRAINING_DIR,
dev=False,
limit=train_limit)
print("Training on", len(train_data), "articles")
print()
dev_data = training_set_creator.read_training(nlp=nlp_2,
training_dir=TRAINING_DIR,
dev=True,
limit=dev_limit)
print("Dev testing on", len(dev_data), "articles")
print()
if not train_data:
print("Did not find any training data")
else:
for itn in range(EPOCHS):
random.shuffle(train_data)
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
batchnr = 0
with nlp_2.disable_pipes(*other_pipes):
for batch in batches:
try:
docs, golds = zip(*batch)
nlp_2.update(
docs,
golds,
sgd=optimizer,
drop=DROPOUT,
losses=losses,
)
batchnr += 1
except Exception as e:
print("Error updating batch:", e)
if batchnr > 0:
with el_pipe.model.use_params(optimizer.averages):
el_pipe.context_weight = 1
el_pipe.prior_weight = 0
dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
losses['entity_linker'] = losses['entity_linker'] / batchnr
print("Epoch, train loss", itn, round(losses['entity_linker'], 2),
" / dev acc context avg", round(dev_acc_context, 3))
# STEP 7: measure the performance of our trained pipe on an independent dev set
if len(dev_data) and measure_performance:
print()
print("STEP 7: performance measurement of Entity Linking pipe", datetime.datetime.now())
print()
acc_r, acc_r_by_label, acc_p, acc_p_by_label, acc_o, acc_o_by_label = _measure_baselines(dev_data, kb_2)
print("dev acc oracle:", round(acc_o, 3), [(x, round(y, 3)) for x, y in acc_o_by_label.items()])
print("dev acc random:", round(acc_r, 3), [(x, round(y, 3)) for x, y in acc_r_by_label.items()])
print("dev acc prior:", round(acc_p, 3), [(x, round(y, 3)) for x, y in acc_p_by_label.items()])
with el_pipe.model.use_params(optimizer.averages):
# measuring combined accuracy (prior + context)
el_pipe.context_weight = 1
el_pipe.prior_weight = 1
dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe)
print("dev acc combo avg:", round(dev_acc_combo, 3),
[(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
# using only context
el_pipe.context_weight = 1
el_pipe.prior_weight = 0
dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
print("dev acc context avg:", round(dev_acc_context, 3),
[(x, round(y, 3)) for x, y in dev_acc_context_dict.items()])
print()
# reset for follow-up tests
el_pipe.context_weight = 1
el_pipe.prior_weight = 1
# STEP 8: apply the EL pipe on a toy example
if to_test_pipeline:
print()
print("STEP 8: applying Entity Linking to toy example", datetime.datetime.now())
print()
run_el_toy_example(nlp=nlp_2)
print()
# STEP 9: write the NLP pipeline (including entity linker) to file
if to_write_nlp:
print()
print("STEP 9: testing NLP IO", datetime.datetime.now())
print()
print("writing to", NLP_2_DIR)
nlp_2.to_disk(NLP_2_DIR)
print()
print("reading from", NLP_2_DIR)
nlp_3 = spacy.load(NLP_2_DIR)
# verify that the IO has gone correctly
if to_read_nlp:
print()
print("running toy example with NLP 2")
run_el_toy_example(nlp=nlp_3)
print()
print("STOP", datetime.datetime.now())
def _measure_accuracy(data, el_pipe):
correct_by_label = dict()
incorrect_by_label = dict()
docs = [d for d, g in data if len(d) > 0]
docs = el_pipe.pipe(docs)
golds = [g for d, g in data if len(d) > 0]
for doc, gold in zip(docs, golds):
try:
correct_entries_per_article = dict()
for entity in gold.links:
start, end, gold_kb = entity
correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
for ent in doc.ents:
ent_label = ent.label_
pred_entity = ent.kb_id_
start = ent.start_char
end = ent.end_char
gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
if gold_entity is not None:
if gold_entity == pred_entity:
correct = correct_by_label.get(ent_label, 0)
correct_by_label[ent_label] = correct + 1
else:
incorrect = incorrect_by_label.get(ent_label, 0)
incorrect_by_label[ent_label] = incorrect + 1
except Exception as e:
print("Error assessing accuracy", e)
acc, acc_by_label = calculate_acc(correct_by_label, incorrect_by_label)
return acc, acc_by_label
def _measure_baselines(data, kb):
# Measure 3 performance baselines: random selection, prior probabilities, and 'oracle' prediction for upper bound
random_correct_by_label = dict()
random_incorrect_by_label = dict()
oracle_correct_by_label = dict()
oracle_incorrect_by_label = dict()
prior_correct_by_label = dict()
prior_incorrect_by_label = dict()
docs = [d for d, g in data if len(d) > 0]
golds = [g for d, g in data if len(d) > 0]
for doc, gold in zip(docs, golds):
try:
correct_entries_per_article = dict()
for entity in gold.links:
start, end, gold_kb = entity
correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
for ent in doc.ents:
ent_label = ent.label_
start = ent.start_char
end = ent.end_char
gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
if gold_entity is not None:
candidates = kb.get_candidates(ent.text)
oracle_candidate = ""
best_candidate = ""
random_candidate = ""
if candidates:
scores = list()
for c in candidates:
scores.append(c.prior_prob)
if c.entity_ == gold_entity:
oracle_candidate = c.entity_
best_index = scores.index(max(scores))
best_candidate = candidates[best_index].entity_
random_candidate = random.choice(candidates).entity_
if gold_entity == best_candidate:
prior_correct_by_label[ent_label] = prior_correct_by_label.get(ent_label, 0) + 1
else:
prior_incorrect_by_label[ent_label] = prior_incorrect_by_label.get(ent_label, 0) + 1
if gold_entity == random_candidate:
random_correct_by_label[ent_label] = random_correct_by_label.get(ent_label, 0) + 1
else:
random_incorrect_by_label[ent_label] = random_incorrect_by_label.get(ent_label, 0) + 1
if gold_entity == oracle_candidate:
oracle_correct_by_label[ent_label] = oracle_correct_by_label.get(ent_label, 0) + 1
else:
oracle_incorrect_by_label[ent_label] = oracle_incorrect_by_label.get(ent_label, 0) + 1
except Exception as e:
print("Error assessing accuracy", e)
acc_prior, acc_prior_by_label = calculate_acc(prior_correct_by_label, prior_incorrect_by_label)
acc_random, acc_random_by_label = calculate_acc(random_correct_by_label, random_incorrect_by_label)
acc_oracle, acc_oracle_by_label = calculate_acc(oracle_correct_by_label, oracle_incorrect_by_label)
return acc_random, acc_random_by_label, acc_prior, acc_prior_by_label, acc_oracle, acc_oracle_by_label
def calculate_acc(correct_by_label, incorrect_by_label):
acc_by_label = dict()
total_correct = 0
total_incorrect = 0
all_keys = set()
all_keys.update(correct_by_label.keys())
all_keys.update(incorrect_by_label.keys())
for label in sorted(all_keys):
correct = correct_by_label.get(label, 0)
incorrect = incorrect_by_label.get(label, 0)
total_correct += correct
total_incorrect += incorrect
if correct == incorrect == 0:
acc_by_label[label] = 0
else:
acc_by_label[label] = correct / (correct + incorrect)
acc = 0
if not (total_correct == total_incorrect == 0):
acc = total_correct / (total_correct + total_incorrect)
return acc, acc_by_label
def check_kb(kb):
for mention in ("Bush", "Douglas Adams", "Homer", "Brazil", "China"):
candidates = kb.get_candidates(mention)
print("generating candidates for " + mention + " :")
for c in candidates:
print(" ", c.prior_prob, c.alias_, "-->", c.entity_ + " (freq=" + str(c.entity_freq) + ")")
print()
def run_el_toy_example(nlp):
text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
"Douglas reminds us to always bring our towel, even in China or Brazil. " \
"The main character in Doug's novel is the man Arthur Dent, " \
"but Douglas doesn't write about George Washington or Homer Simpson."
doc = nlp(text)
print(text)
for ent in doc.ents:
print("ent", ent.text, ent.label_, ent.kb_id_)
print()
# Q4426480 is her husband
text = "Ada Lovelace was the countess of Lovelace. She's known for her programming work on the analytical engine. "\
"She loved her husband William King dearly. "
doc = nlp(text)
print(text)
for ent in doc.ents:
print("ent", ent.text, ent.label_, ent.kb_id_)
print()
# Q3568763 is her tutor
text = "Ada Lovelace was the countess of Lovelace. She's known for her programming work on the analytical engine. "\
"She was tutored by her favorite physics tutor William King."
doc = nlp(text)
print(text)
for ent in doc.ents:
print("ent", ent.text, ent.label_, ent.kb_id_)
if __name__ == "__main__":
run_pipeline()
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