mirror of
https://github.com/explosion/spaCy.git
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fcbf899b08
* remove _convert_examples * fix test_gold, raise TypeError if tuples are used instead of Example's * throwing proper errors when the wrong type of objects are passed * fix deprectated format in tests * fix deprectated format in parser tests * fix tests for NEL, morph, senter, tagger, textcat * update regression tests with new Example format * use make_doc * more fixes to nlp.update calls * few more small fixes for rehearse and evaluate * only import ml_datasets if really necessary
177 lines
7.1 KiB
Python
177 lines
7.1 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Example of training spaCy's entity linker, starting off with a predefined
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knowledge base and corresponding vocab, and a blank English model.
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For more details, see the documentation:
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* Training: https://spacy.io/usage/training
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* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
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Compatible with: spaCy v2.2.4
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Last tested with: v2.2.4
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"""
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from __future__ import unicode_literals, print_function
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import plac
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import random
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from pathlib import Path
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from spacy.vocab import Vocab
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import spacy
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from spacy.kb import KnowledgeBase
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from spacy.gold import Example
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from spacy.pipeline import EntityRuler
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from spacy.util import minibatch, compounding
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def sample_train_data():
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train_data = []
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# Q2146908 (Russ Cochran): American golfer
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# Q7381115 (Russ Cochran): publisher
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text_1 = "Russ Cochran his reprints include EC Comics."
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dict_1 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
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train_data.append((text_1, {"links": dict_1}))
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text_2 = "Russ Cochran has been publishing comic art."
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dict_2 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
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train_data.append((text_2, {"links": dict_2}))
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text_3 = "Russ Cochran captured his first major title with his son as caddie."
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dict_3 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
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train_data.append((text_3, {"links": dict_3}))
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text_4 = "Russ Cochran was a member of University of Kentucky's golf team."
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dict_4 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
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train_data.append((text_4, {"links": dict_4}))
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return train_data
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# training data
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TRAIN_DATA = sample_train_data()
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@plac.annotations(
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kb_path=("Path to the knowledge base", "positional", None, Path),
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vocab_path=("Path to the vocab for the kb", "positional", None, Path),
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output_dir=("Optional output directory", "option", "o", Path),
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n_iter=("Number of training iterations", "option", "n", int),
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)
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def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
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"""Create a blank model with the specified vocab, set up the pipeline and train the entity linker.
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The `vocab` should be the one used during creation of the KB."""
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vocab = Vocab().from_disk(vocab_path)
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# create blank English model with correct vocab
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nlp = spacy.blank("en", vocab=vocab)
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nlp.vocab.vectors.name = "nel_vectors"
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print("Created blank 'en' model with vocab from '%s'" % vocab_path)
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# Add a sentencizer component. Alternatively, add a dependency parser for higher accuracy.
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nlp.add_pipe(nlp.create_pipe("sentencizer"))
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# Add a custom component to recognize "Russ Cochran" as an entity for the example training data.
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# Note that in a realistic application, an actual NER algorithm should be used instead.
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ruler = EntityRuler(nlp)
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patterns = [
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{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]}
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]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler)
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# Create the Entity Linker component and add it to the pipeline.
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if "entity_linker" not in nlp.pipe_names:
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kb = KnowledgeBase(vocab=nlp.vocab)
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kb.load_bulk(kb_path)
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print("Loaded Knowledge Base from '%s'" % kb_path)
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# use only the predicted EL score and not the prior probability (for demo purposes)
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cfg = {"kb": kb, "incl_prior": False}
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entity_linker = nlp.create_pipe("entity_linker", cfg)
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nlp.add_pipe(entity_linker, last=True)
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# Convert the texts to docs to make sure we have doc.ents set for the training examples.
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# Also ensure that the annotated examples correspond to known identifiers in the knowledge base.
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kb_ids = nlp.get_pipe("entity_linker").kb.get_entity_strings()
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train_examples = []
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for text, annotation in TRAIN_DATA:
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with nlp.select_pipes(disable="entity_linker"):
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doc = nlp(text)
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annotation_clean = annotation
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for offset, kb_id_dict in annotation["links"].items():
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new_dict = {}
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for kb_id, value in kb_id_dict.items():
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if kb_id in kb_ids:
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new_dict[kb_id] = value
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else:
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print(
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"Removed", kb_id, "from training because it is not in the KB."
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)
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annotation_clean["links"][offset] = new_dict
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train_examples .append(Example.from_dict(doc, annotation_clean))
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with nlp.select_pipes(enable="entity_linker"): # only train entity linker
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# reset and initialize the weights randomly
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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random.shuffle(train_examples)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(train_examples, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(
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batch,
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drop=0.2, # dropout - make it harder to memorise data
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losses=losses,
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sgd=optimizer,
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)
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print(itn, "Losses", losses)
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# test the trained model
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_apply_model(nlp)
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# save model to output directory
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if output_dir is not None:
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output_dir = Path(output_dir)
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if not output_dir.exists():
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output_dir.mkdir()
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nlp.to_disk(output_dir)
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print()
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print("Saved model to", output_dir)
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# test the saved model
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print("Loading from", output_dir)
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nlp2 = spacy.load(output_dir)
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_apply_model(nlp2)
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def _apply_model(nlp):
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for text, annotation in TRAIN_DATA:
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# apply the entity linker which will now make predictions for the 'Russ Cochran' entities
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doc = nlp(text)
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print()
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print("Entities", [(ent.text, ent.label_, ent.kb_id_) for ent in doc.ents])
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print("Tokens", [(t.text, t.ent_type_, t.ent_kb_id_) for t in doc])
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if __name__ == "__main__":
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plac.call(main)
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# Expected output (can be shuffled):
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# Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
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# Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ("his", '', ''), ('reprints', '', ''), ('include', '', ''), ('The', '', ''), ('Complete', '', ''), ('EC', '', ''), ('Library', '', ''), ('.', '', '')]
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# Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
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# Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ('has', '', ''), ('been', '', ''), ('publishing', '', ''), ('comic', '', ''), ('art', '', ''), ('.', '', '')]
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# Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
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# Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('captured', '', ''), ('his', '', ''), ('first', '', ''), ('major', '', ''), ('title', '', ''), ('with', '', ''), ('his', '', ''), ('son', '', ''), ('as', '', ''), ('caddie', '', ''), ('.', '', '')]
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# Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
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# Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('was', '', ''), ('a', '', ''), ('member', '', ''), ('of', '', ''), ('University', '', ''), ('of', '', ''), ('Kentucky', '', ''), ("'s", '', ''), ('golf', '', ''), ('team', '', ''), ('.', '', '')]
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