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503 lines
18 KiB
Python
503 lines
18 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import random
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import datetime
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from pathlib import Path
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from bin.wiki_entity_linking import wikipedia_processor as wp
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from bin.wiki_entity_linking import training_set_creator, kb_creator
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from bin.wiki_entity_linking.kb_creator import DESC_WIDTH
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import spacy
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from spacy.kb import KnowledgeBase
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from spacy.util import minibatch, compounding
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"""
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Demonstrate how to build a knowledge base from WikiData and run an Entity Linking algorithm.
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"""
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ROOT_DIR = Path("C:/Users/Sofie/Documents/data/")
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OUTPUT_DIR = ROOT_DIR / "wikipedia"
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TRAINING_DIR = OUTPUT_DIR / "training_data_nel"
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PRIOR_PROB = OUTPUT_DIR / "prior_prob.csv"
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ENTITY_COUNTS = OUTPUT_DIR / "entity_freq.csv"
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ENTITY_DEFS = OUTPUT_DIR / "entity_defs.csv"
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ENTITY_DESCR = OUTPUT_DIR / "entity_descriptions.csv"
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KB_FILE = OUTPUT_DIR / "kb_1" / "kb"
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NLP_1_DIR = OUTPUT_DIR / "nlp_1"
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NLP_2_DIR = OUTPUT_DIR / "nlp_2"
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# get latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
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WIKIDATA_JSON = ROOT_DIR / "wikidata" / "wikidata-20190304-all.json.bz2"
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# get enwiki-latest-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/enwiki/latest/
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ENWIKI_DUMP = (
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ROOT_DIR / "wikipedia" / "enwiki-20190320-pages-articles-multistream.xml.bz2"
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)
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# KB construction parameters
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MAX_CANDIDATES = 10
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MIN_ENTITY_FREQ = 20
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MIN_PAIR_OCC = 5
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# model training parameters
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EPOCHS = 10
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DROPOUT = 0.5
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LEARN_RATE = 0.005
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L2 = 1e-6
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CONTEXT_WIDTH = 128
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def now():
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return datetime.datetime.now()
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def run_pipeline():
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# set the appropriate booleans to define which parts of the pipeline should be re(run)
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print("START", now())
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print()
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nlp_1 = spacy.load("en_core_web_lg")
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nlp_2 = None
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kb_2 = None
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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 = 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 = False
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# create training dataset
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create_wp_training = False
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# train the EL pipe
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train_pipe = True
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measure_performance = True
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# test the EL pipe on a simple example
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to_test_pipeline = True
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# write the NLP object, read back in and test again
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to_write_nlp = True
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to_read_nlp = True
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test_from_file = False
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# STEP 1 : create prior probabilities from WP (run only once)
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if to_create_prior_probs:
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print("STEP 1: to_create_prior_probs", now())
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wp.read_prior_probs(ENWIKI_DUMP, PRIOR_PROB)
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print()
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# STEP 2 : deduce entity frequencies from WP (run only once)
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if to_create_entity_counts:
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print("STEP 2: to_create_entity_counts", now())
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wp.write_entity_counts(PRIOR_PROB, ENTITY_COUNTS, to_print=False)
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print()
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# STEP 3 : create KB and write to file (run only once)
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if to_create_kb:
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print("STEP 3a: to_create_kb", now())
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kb_1 = kb_creator.create_kb(
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nlp=nlp_1,
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max_entities_per_alias=MAX_CANDIDATES,
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min_entity_freq=MIN_ENTITY_FREQ,
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min_occ=MIN_PAIR_OCC,
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entity_def_output=ENTITY_DEFS,
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entity_descr_output=ENTITY_DESCR,
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count_input=ENTITY_COUNTS,
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prior_prob_input=PRIOR_PROB,
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wikidata_input=WIKIDATA_JSON,
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)
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print("kb entities:", kb_1.get_size_entities())
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print("kb aliases:", kb_1.get_size_aliases())
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print()
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print("STEP 3b: write KB and NLP", now())
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kb_1.dump(KB_FILE)
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nlp_1.to_disk(NLP_1_DIR)
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print()
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# STEP 4 : read KB back in from file
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if to_read_kb:
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print("STEP 4: to_read_kb", now())
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nlp_2 = spacy.load(NLP_1_DIR)
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kb_2 = KnowledgeBase(vocab=nlp_2.vocab, entity_vector_length=DESC_WIDTH)
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kb_2.load_bulk(KB_FILE)
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print("kb entities:", kb_2.get_size_entities())
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print("kb aliases:", kb_2.get_size_aliases())
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print()
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# test KB
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if to_test_kb:
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check_kb(kb_2)
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print()
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# STEP 5: create a training dataset from WP
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if create_wp_training:
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print("STEP 5: create training dataset", now())
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training_set_creator.create_training(
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wikipedia_input=ENWIKI_DUMP,
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entity_def_input=ENTITY_DEFS,
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training_output=TRAINING_DIR,
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)
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# STEP 6: create and train the entity linking pipe
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if train_pipe:
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print("STEP 6: training Entity Linking pipe", now())
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type_to_int = {label: i for i, label in enumerate(nlp_2.entity.labels)}
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print(" -analysing", len(type_to_int), "different entity types")
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el_pipe = nlp_2.create_pipe(
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name="entity_linker",
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config={
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"context_width": CONTEXT_WIDTH,
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"pretrained_vectors": nlp_2.vocab.vectors.name,
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"type_to_int": type_to_int,
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},
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)
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el_pipe.set_kb(kb_2)
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nlp_2.add_pipe(el_pipe, last=True)
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other_pipes = [pipe for pipe in nlp_2.pipe_names if pipe != "entity_linker"]
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with nlp_2.disable_pipes(*other_pipes): # only train Entity Linking
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optimizer = nlp_2.begin_training()
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optimizer.learn_rate = LEARN_RATE
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optimizer.L2 = L2
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# define the size (nr of entities) of training and dev set
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train_limit = 5000
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dev_limit = 5000
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# for training, get pos & neg instances that correspond to entries in the kb
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train_data = training_set_creator.read_training(
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nlp=nlp_2,
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training_dir=TRAINING_DIR,
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dev=False,
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limit=train_limit,
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kb=el_pipe.kb,
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)
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print("Training on", len(train_data), "articles")
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print()
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# for testing, get all pos instances, whether or not they are in the kb
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dev_data = training_set_creator.read_training(
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nlp=nlp_2, training_dir=TRAINING_DIR, dev=True, limit=dev_limit, kb=None
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)
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print("Dev testing on", len(dev_data), "articles")
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print()
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if not train_data:
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print("Did not find any training data")
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else:
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for itn in range(EPOCHS):
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random.shuffle(train_data)
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losses = {}
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batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
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batchnr = 0
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with nlp_2.disable_pipes(*other_pipes):
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for batch in batches:
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try:
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docs, golds = zip(*batch)
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nlp_2.update(
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docs=docs,
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golds=golds,
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sgd=optimizer,
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drop=DROPOUT,
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losses=losses,
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)
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batchnr += 1
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except Exception as e:
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print("Error updating batch:", e)
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if batchnr > 0:
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el_pipe.cfg["context_weight"] = 1
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el_pipe.cfg["prior_weight"] = 1
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dev_acc_context, _ = _measure_acc(dev_data, el_pipe)
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losses["entity_linker"] = losses["entity_linker"] / batchnr
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print(
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"Epoch, train loss",
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itn,
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round(losses["entity_linker"], 2),
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" / dev acc avg",
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round(dev_acc_context, 3),
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)
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# STEP 7: measure the performance of our trained pipe on an independent dev set
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if len(dev_data) and measure_performance:
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print()
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print("STEP 7: performance measurement of Entity Linking pipe", now())
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print()
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counts, acc_r, acc_r_d, acc_p, acc_p_d, acc_o, acc_o_d = _measure_baselines(
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dev_data, kb_2
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)
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print("dev counts:", sorted(counts.items(), key=lambda x: x[0]))
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oracle_by_label = [(x, round(y, 3)) for x, y in acc_o_d.items()]
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print("dev acc oracle:", round(acc_o, 3), oracle_by_label)
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random_by_label = [(x, round(y, 3)) for x, y in acc_r_d.items()]
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print("dev acc random:", round(acc_r, 3), random_by_label)
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prior_by_label = [(x, round(y, 3)) for x, y in acc_p_d.items()]
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print("dev acc prior:", round(acc_p, 3), prior_by_label)
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# using only context
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el_pipe.cfg["context_weight"] = 1
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el_pipe.cfg["prior_weight"] = 0
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dev_acc_context, dev_acc_cont_d = _measure_acc(dev_data, el_pipe)
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context_by_label = [(x, round(y, 3)) for x, y in dev_acc_cont_d.items()]
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print("dev acc context avg:", round(dev_acc_context, 3), context_by_label)
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# measuring combined accuracy (prior + context)
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el_pipe.cfg["context_weight"] = 1
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el_pipe.cfg["prior_weight"] = 1
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dev_acc_combo, dev_acc_combo_d = _measure_acc(dev_data, el_pipe)
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combo_by_label = [(x, round(y, 3)) for x, y in dev_acc_combo_d.items()]
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print("dev acc combo avg:", round(dev_acc_combo, 3), combo_by_label)
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# STEP 8: apply the EL pipe on a toy example
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if to_test_pipeline:
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print()
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print("STEP 8: applying Entity Linking to toy example", now())
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print()
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run_el_toy_example(nlp=nlp_2)
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# STEP 9: write the NLP pipeline (including entity linker) to file
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if to_write_nlp:
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print()
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print("STEP 9: testing NLP IO", now())
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print()
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print("writing to", NLP_2_DIR)
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nlp_2.to_disk(NLP_2_DIR)
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print()
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# verify that the IO has gone correctly
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if to_read_nlp:
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print("reading from", NLP_2_DIR)
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nlp_3 = spacy.load(NLP_2_DIR)
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print("running toy example with NLP 3")
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run_el_toy_example(nlp=nlp_3)
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# testing performance with an NLP model from file
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if test_from_file:
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nlp_2 = spacy.load(NLP_1_DIR)
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nlp_3 = spacy.load(NLP_2_DIR)
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el_pipe = nlp_3.get_pipe("entity_linker")
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dev_limit = 5000
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dev_data = training_set_creator.read_training(
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nlp=nlp_2, training_dir=TRAINING_DIR, dev=True, limit=dev_limit, kb=None
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)
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print("Dev testing from file on", len(dev_data), "articles")
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print()
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dev_acc_combo, dev_acc_combo_dict = _measure_acc(dev_data, el_pipe)
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combo_by_label = [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()]
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print("dev acc combo avg:", round(dev_acc_combo, 3), combo_by_label)
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print()
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print("STOP", now())
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def _measure_acc(data, el_pipe=None, error_analysis=False):
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# If the docs in the data require further processing with an entity linker, set el_pipe
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correct_by_label = dict()
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incorrect_by_label = dict()
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docs = [d for d, g in data if len(d) > 0]
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if el_pipe is not None:
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docs = list(el_pipe.pipe(docs))
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golds = [g for d, g in data if len(d) > 0]
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for doc, gold in zip(docs, golds):
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try:
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correct_entries_per_article = dict()
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for entity, kb_dict in gold.links.items():
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start, end = entity
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# only evaluating on positive examples
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for gold_kb, value in kb_dict.items():
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if value:
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correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
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for ent in doc.ents:
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ent_label = ent.label_
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pred_entity = ent.kb_id_
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start = ent.start_char
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end = ent.end_char
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offset = str(start) + "-" + str(end)
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gold_entity = correct_entries_per_article.get(offset, None)
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# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
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if gold_entity is not None:
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if gold_entity == pred_entity:
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correct = correct_by_label.get(ent_label, 0)
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correct_by_label[ent_label] = correct + 1
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else:
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incorrect = incorrect_by_label.get(ent_label, 0)
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incorrect_by_label[ent_label] = incorrect + 1
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if error_analysis:
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print(ent.text, "in", doc)
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print(
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"Predicted",
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pred_entity,
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"should have been",
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gold_entity,
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)
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print()
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except Exception as e:
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print("Error assessing accuracy", e)
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acc, acc_by_label = calculate_acc(correct_by_label, incorrect_by_label)
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return acc, acc_by_label
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def _measure_baselines(data, kb):
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# Measure 3 performance baselines: random selection, prior probabilities, and 'oracle' prediction for upper bound
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counts_d = dict()
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random_correct_d = dict()
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random_incorrect_d = dict()
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oracle_correct_d = dict()
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oracle_incorrect_d = dict()
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prior_correct_d = dict()
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prior_incorrect_d = dict()
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docs = [d for d, g in data if len(d) > 0]
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golds = [g for d, g in data if len(d) > 0]
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for doc, gold in zip(docs, golds):
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try:
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correct_entries_per_article = dict()
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for entity, kb_dict in gold.links.items():
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start, end = entity
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for gold_kb, value in kb_dict.items():
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# only evaluating on positive examples
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if value:
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correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
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for ent in doc.ents:
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label = ent.label_
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start = ent.start_char
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end = ent.end_char
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offset = str(start) + "-" + str(end)
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gold_entity = correct_entries_per_article.get(offset, None)
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# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
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if gold_entity is not None:
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counts_d[label] = counts_d.get(label, 0) + 1
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candidates = kb.get_candidates(ent.text)
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oracle_candidate = ""
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best_candidate = ""
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random_candidate = ""
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if candidates:
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scores = []
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for c in candidates:
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scores.append(c.prior_prob)
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if c.entity_ == gold_entity:
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oracle_candidate = c.entity_
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best_index = scores.index(max(scores))
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best_candidate = candidates[best_index].entity_
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random_candidate = random.choice(candidates).entity_
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if gold_entity == best_candidate:
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prior_correct_d[label] = prior_correct_d.get(label, 0) + 1
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else:
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prior_incorrect_d[label] = prior_incorrect_d.get(label, 0) + 1
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if gold_entity == random_candidate:
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random_correct_d[label] = random_correct_d.get(label, 0) + 1
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else:
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random_incorrect_d[label] = random_incorrect_d.get(label, 0) + 1
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if gold_entity == oracle_candidate:
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oracle_correct_d[label] = oracle_correct_d.get(label, 0) + 1
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else:
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oracle_incorrect_d[label] = oracle_incorrect_d.get(label, 0) + 1
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except Exception as e:
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print("Error assessing accuracy", e)
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acc_prior, acc_prior_d = calculate_acc(prior_correct_d, prior_incorrect_d)
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acc_rand, acc_rand_d = calculate_acc(random_correct_d, random_incorrect_d)
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acc_oracle, acc_oracle_d = calculate_acc(oracle_correct_d, oracle_incorrect_d)
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return (
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counts_d,
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acc_rand,
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acc_rand_d,
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acc_prior,
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acc_prior_d,
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acc_oracle,
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acc_oracle_d,
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)
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def calculate_acc(correct_by_label, incorrect_by_label):
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acc_by_label = dict()
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total_correct = 0
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total_incorrect = 0
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all_keys = set()
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all_keys.update(correct_by_label.keys())
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all_keys.update(incorrect_by_label.keys())
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for label in sorted(all_keys):
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correct = correct_by_label.get(label, 0)
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incorrect = incorrect_by_label.get(label, 0)
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total_correct += correct
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total_incorrect += incorrect
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if correct == incorrect == 0:
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acc_by_label[label] = 0
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else:
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acc_by_label[label] = correct / (correct + incorrect)
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acc = 0
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if not (total_correct == total_incorrect == 0):
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acc = total_correct / (total_correct + total_incorrect)
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return acc, acc_by_label
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def check_kb(kb):
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for mention in ("Bush", "Douglas Adams", "Homer", "Brazil", "China"):
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candidates = kb.get_candidates(mention)
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print("generating candidates for " + mention + " :")
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for c in candidates:
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print(
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" ",
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c.prior_prob,
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c.alias_,
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|
"-->",
|
|
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 Dougledydoug 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()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_pipeline()
|