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#!/usr/bin/env python
# coding: utf8
"""Example of defining and (pre)training spaCy's knowledge base,
which is needed to implement entity linking functionality.
For more details, see the documentation:
* Knowledge base: https://spacy.io/api/kb
* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
Compatible with: spaCy v2.2
Last tested with: v2.2
"""
from __future__ import unicode_literals, print_function
import plac
from pathlib import Path
from spacy.vocab import Vocab
import spacy
from spacy.kb import KnowledgeBase
from bin.wiki_entity_linking.train_descriptions import EntityEncoder
# Q2146908 (Russ Cochran): American golfer
# Q7381115 (Russ Cochran): publisher
ENTITIES = {"Q2146908": ("American golfer", 342), "Q7381115": ("publisher", 17)}
INPUT_DIM = 300 # dimension of pretrained input vectors
DESC_WIDTH = 64 # dimension of output entity vectors
@plac.annotations(
vocab_path=("Path to the vocab for the kb", "option", "v", Path),
model=("Model name, should have pretrained word embeddings", "option", "m", str),
output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int),
)
def main(vocab_path=None, model=None, output_dir=None, n_iter=50):
"""Load the model, create the KB and pretrain the entity encodings.
Either an nlp model or a vocab is needed to provide access to pretrained word embeddings.
If an output_dir is provided, the KB will be stored there in a file 'kb'.
When providing an nlp model, the updated vocab will also be written to a directory in the output_dir."""
if model is None and vocab_path is None:
raise ValueError("Either the `nlp` model or the `vocab` should be specified.")
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
vocab = Vocab().from_disk(vocab_path)
# create blank Language class with specified vocab
nlp = spacy.blank("en", vocab=vocab)
print("Created blank 'en' model with vocab from '%s'" % vocab_path)
kb = KnowledgeBase(vocab=nlp.vocab)
# set up the data
entity_ids = []
descriptions = []
freqs = []
for key, value in ENTITIES.items():
desc, freq = value
entity_ids.append(key)
descriptions.append(desc)
freqs.append(freq)
# training entity description encodings
# this part can easily be replaced with a custom entity encoder
encoder = EntityEncoder(
nlp=nlp,
input_dim=INPUT_DIM,
desc_width=DESC_WIDTH,
epochs=n_iter,
)
encoder.train(description_list=descriptions, to_print=True)
# get the pretrained entity vectors
embeddings = encoder.apply_encoder(descriptions)
# set the entities, can also be done by calling `kb.add_entity` for each entity
kb.set_entities(entity_list=entity_ids, freq_list=freqs, vector_list=embeddings)
# adding aliases, the entities need to be defined in the KB beforehand
kb.add_alias(
alias="Russ Cochran",
entities=["Q2146908", "Q7381115"],
probabilities=[0.24, 0.7], # the sum of these probabilities should not exceed 1
)
# test the trained model
print()
_print_kb(kb)
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
kb_path = str(output_dir / "kb")
kb.dump(kb_path)
print()
print("Saved KB to", kb_path)
# only storing the vocab if we weren't already reading it from file
if not vocab_path:
vocab_path = output_dir / "vocab"
kb.vocab.to_disk(vocab_path)
print("Saved vocab to", vocab_path)
print()
# test the saved model
# always reload a knowledge base with the same vocab instance!
print("Loading vocab from", vocab_path)
print("Loading KB from", kb_path)
vocab2 = Vocab().from_disk(vocab_path)
kb2 = KnowledgeBase(vocab=vocab2)
kb2.load_bulk(kb_path)
_print_kb(kb2)
print()
def _print_kb(kb):
print(kb.get_size_entities(), "kb entities:", kb.get_entity_strings())
print(kb.get_size_aliases(), "kb aliases:", kb.get_alias_strings())
if __name__ == "__main__":
plac.call(main)
# Expected output:
# 2 kb entities: ['Q2146908', 'Q7381115']
# 1 kb aliases: ['Russ Cochran']
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