mirror of https://github.com/explosion/spaCy.git
450 lines
18 KiB
Python
450 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 training_set_creator, kb_creator, wikipedia_processor as wp
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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 = ROOT_DIR / 'wikipedia' / 'enwiki-20190320-pages-articles-multistream.xml.bz2'
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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 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", datetime.datetime.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", datetime.datetime.now())
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wp.read_wikipedia_prior_probs(wikipedia_input=ENWIKI_DUMP, prior_prob_output=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", datetime.datetime.now())
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wp.write_entity_counts(prior_prob_input=PRIOR_PROB, count_output=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", datetime.datetime.now())
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kb_1 = kb_creator.create_kb(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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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", datetime.datetime.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", datetime.datetime.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", datetime.datetime.now())
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training_set_creator.create_training(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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# 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", datetime.datetime.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(name='entity_linker',
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config={"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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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 = 5
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dev_limit = 5
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train_data = training_set_creator.read_training(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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print("Training on", len(train_data), "articles")
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print()
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dev_data = training_set_creator.read_training(nlp=nlp_2,
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training_dir=TRAINING_DIR,
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dev=True,
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limit=dev_limit)
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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,
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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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with el_pipe.model.use_params(optimizer.averages):
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 1
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dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
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losses['entity_linker'] = losses['entity_linker'] / batchnr
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print("Epoch, train loss", itn, round(losses['entity_linker'], 2),
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" / dev acc avg", round(dev_acc_context, 3))
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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", datetime.datetime.now())
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print()
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counts, acc_r, acc_r_label, acc_p, acc_p_label, acc_o, acc_o_label = _measure_baselines(dev_data, kb_2)
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print("dev counts:", sorted(counts.items(), key=lambda x: x[0]))
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print("dev acc oracle:", round(acc_o, 3), [(x, round(y, 3)) for x, y in acc_o_label.items()])
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print("dev acc random:", round(acc_r, 3), [(x, round(y, 3)) for x, y in acc_r_label.items()])
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print("dev acc prior:", round(acc_p, 3), [(x, round(y, 3)) for x, y in acc_p_label.items()])
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with el_pipe.model.use_params(optimizer.averages):
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# measuring combined accuracy (prior + context)
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 1
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dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe, error_analysis=False)
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print("dev acc combo avg:", round(dev_acc_combo, 3),
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[(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
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# using only context
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 0
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dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc context avg:", round(dev_acc_context, 3),
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[(x, round(y, 3)) for x, y in dev_acc_context_dict.items()])
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# reset for follow-up tests
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 1
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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", datetime.datetime.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", datetime.datetime.now())
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print()
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print("writing to", NLP_2_DIR)
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with el_pipe.model.use_params(optimizer.averages) and el_pipe.model.tok2vec.use_params(el_pipe.sgd_context.averages):
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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(nlp=nlp_2,
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training_dir=TRAINING_DIR,
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dev=True,
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limit=dev_limit)
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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_accuracy(dev_data, el_pipe=el_pipe, error_analysis=False)
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print("dev acc combo avg:", round(dev_acc_combo, 3),
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[(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
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print()
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print("STOP", datetime.datetime.now())
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def _measure_accuracy(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 in gold.links:
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start, end, gold_kb = entity
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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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gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), 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("Predicted", pred_entity, "should have been", gold_entity)
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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_by_label = dict()
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random_correct_by_label = dict()
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random_incorrect_by_label = dict()
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oracle_correct_by_label = dict()
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oracle_incorrect_by_label = dict()
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prior_correct_by_label = dict()
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prior_incorrect_by_label = 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 in gold.links:
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start, end, gold_kb = entity
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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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start = ent.start_char
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end = ent.end_char
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gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), 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_by_label[ent_label] = counts_by_label.get(ent_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_by_label[ent_label] = prior_correct_by_label.get(ent_label, 0) + 1
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else:
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prior_incorrect_by_label[ent_label] = prior_incorrect_by_label.get(ent_label, 0) + 1
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if gold_entity == random_candidate:
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random_correct_by_label[ent_label] = random_correct_by_label.get(ent_label, 0) + 1
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else:
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random_incorrect_by_label[ent_label] = random_incorrect_by_label.get(ent_label, 0) + 1
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if gold_entity == oracle_candidate:
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oracle_correct_by_label[ent_label] = oracle_correct_by_label.get(ent_label, 0) + 1
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else:
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oracle_incorrect_by_label[ent_label] = oracle_incorrect_by_label.get(ent_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_by_label = calculate_acc(prior_correct_by_label, prior_incorrect_by_label)
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acc_rand, acc_rand_by_label = calculate_acc(random_correct_by_label, random_incorrect_by_label)
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acc_oracle, acc_oracle_by_label = calculate_acc(oracle_correct_by_label, oracle_incorrect_by_label)
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return counts_by_label, acc_rand, acc_rand_by_label, acc_prior, acc_prior_by_label, acc_oracle, acc_oracle_by_label
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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(" ", c.prior_prob, c.alias_, "-->", c.entity_ + " (freq=" + str(c.entity_freq) + ")")
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print()
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def run_el_toy_example(nlp):
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text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
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"Douglas reminds us to always bring our towel, even in China or Brazil. " \
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"The main character in Doug's novel is the man Arthur Dent, " \
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"but Douglas doesn't write about George Washington or Homer Simpson."
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doc = nlp(text)
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print(text)
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for ent in doc.ents:
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print(" ent", ent.text, ent.label_, ent.kb_id_)
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print()
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if __name__ == "__main__":
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run_pipeline()
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