mirror of
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247 lines
8.5 KiB
Python
247 lines
8.5 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import random
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from spacy.util import minibatch, compounding
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from examples.pipeline.wiki_entity_linking import wikipedia_processor as wp, kb_creator, training_set_creator, run_el
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import spacy
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from spacy.vocab import Vocab
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from spacy.kb import KnowledgeBase
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import datetime
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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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PRIOR_PROB = 'C:/Users/Sofie/Documents/data/wikipedia/prior_prob.csv'
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ENTITY_COUNTS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_freq.csv'
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ENTITY_DEFS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_defs.csv'
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ENTITY_DESCR = 'C:/Users/Sofie/Documents/data/wikipedia/entity_descriptions.csv'
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KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb'
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VOCAB_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/vocab'
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TRAINING_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/training_data_nel/'
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MAX_CANDIDATES = 10
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MIN_PAIR_OCC = 5
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DOC_CHAR_CUTOFF = 300
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EPOCHS = 10
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DROPOUT = 0.1
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def run_pipeline():
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print("START", datetime.datetime.now())
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print()
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nlp = spacy.load('en_core_web_lg')
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my_kb = 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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# test the EL pipe on a simple example
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to_test_pipeline = True
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# STEP 1 : create prior probabilities from WP
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# 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(prior_prob_output=PRIOR_PROB)
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print()
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# STEP 2 : deduce entity frequencies from WP
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# 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
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# 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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my_kb = kb_creator.create_kb(nlp,
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max_entities_per_alias=MAX_CANDIDATES,
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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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to_print=False)
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print("kb entities:", my_kb.get_size_entities())
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print("kb aliases:", my_kb.get_size_aliases())
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print()
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print("STEP 3b: write KB", datetime.datetime.now())
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my_kb.dump(KB_FILE)
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nlp.vocab.to_disk(VOCAB_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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my_vocab = Vocab()
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my_vocab.from_disk(VOCAB_DIR)
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my_kb = KnowledgeBase(vocab=my_vocab, entity_vector_length=64) # TODO entity vectors
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my_kb.load_bulk(KB_FILE)
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print("kb entities:", my_kb.get_size_entities())
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print("kb aliases:", my_kb.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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run_el.run_kb_toy_example(kb=my_kb)
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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(kb=my_kb, entity_def_input=ENTITY_DEFS, training_output=TRAINING_DIR)
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# STEP 6: create the entity linking pipe
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if train_pipe:
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train_limit = 100
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dev_limit = 20
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print("Training on", train_limit, "articles")
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print("Dev testing on", dev_limit, "articles")
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print()
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train_data = training_set_creator.read_training(nlp=nlp,
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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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to_print=False)
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dev_data = training_set_creator.read_training(nlp=nlp,
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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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to_print=False)
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el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": my_kb, "doc_cutoff": DOC_CHAR_CUTOFF})
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nlp.add_pipe(el_pipe, last=True)
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "entity_linker"]
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with nlp.disable_pipes(*other_pipes): # only train Entity Linking
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nlp.begin_training()
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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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with nlp.disable_pipes(*other_pipes):
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for batch in batches:
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docs, golds = zip(*batch)
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nlp.update(
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docs,
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golds,
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drop=DROPOUT,
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losses=losses,
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)
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# print(" measuring accuracy 1-1")
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 1
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dev_acc_1_1 = _measure_accuracy(dev_data, el_pipe)
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train_acc_1_1 = _measure_accuracy(train_data, el_pipe)
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# print(" measuring accuracy 0-1")
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el_pipe.context_weight = 0
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el_pipe.prior_weight = 1
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dev_acc_0_1 = _measure_accuracy(dev_data, el_pipe)
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train_acc_0_1 = _measure_accuracy(train_data, el_pipe)
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# print(" measuring accuracy 1-0")
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 0
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dev_acc_1_0 = _measure_accuracy(dev_data, el_pipe)
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train_acc_1_0 = _measure_accuracy(train_data, el_pipe)
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print("Epoch, train loss, train/dev acc, 1-1, 0-1, 1-0:", itn, round(losses['entity_linker'], 2),
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round(train_acc_1_1, 2), round(train_acc_0_1, 2), round(train_acc_1_0, 2), "/",
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round(dev_acc_1_1, 2), round(dev_acc_0_1, 2), round(dev_acc_1_0, 2))
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# test Entity Linker
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if to_test_pipeline:
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print()
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run_el_toy_example(kb=my_kb, nlp=nlp)
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print()
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print()
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print("STOP", datetime.datetime.now())
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def _measure_accuracy(data, el_pipe):
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correct = 0
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incorrect = 0
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docs = [d for d, g in data]
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docs = el_pipe.pipe(docs)
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golds = [g for d, g in data]
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for doc, gold in zip(docs, golds):
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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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if ent.label_ == "PERSON": # TODO: expand to other types
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pred_entity = ent.kb_id_
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start = ent.start
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end = ent.end
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gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
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if gold_entity is not None:
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if gold_entity == pred_entity:
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correct += 1
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else:
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incorrect += 1
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if correct == incorrect == 0:
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return 0
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acc = correct / (correct + incorrect)
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return acc
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def run_el_toy_example(nlp, kb):
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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. " \
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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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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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# Q4426480 is her husband, Q3568763 her tutor
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text = "Ada Lovelace loved her husband William King dearly. " \
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"Ada Lovelace was tutored by her favorite physics tutor William King."
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doc = nlp(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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if __name__ == "__main__":
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run_pipeline()
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