text: #!/usr/bin/env python
  # coding: utf8
  
  """Example of training spaCy's entity linker, starting off with a predefined
  knowledge base and corresponding vocab, and a blank English model.
  
  For more details, see the documentation:
  * Training: https://spacy.io/usage/training
  * Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
  
  Compatible with: spaCy v2.2.4
  Last tested with: v2.3.4
  """
  from __future__ import unicode_literals, print_function
  
  import plac
  import random
  from pathlib import Path
  
  from spacy.vocab import Vocab
  
  import spacy
  from spacy.kb import KnowledgeBase
  from spacy.pipeline import EntityRuler
  from spacy.util import minibatch, compounding
  
  
  def sample_train_data():
      train_data = []
  
      # Q2146908 (Russ Cochran): American golfer
      # Q7381115 (Russ Cochran): publisher
  
      text_1 = "Russ Cochran his reprints include EC Comics."
      dict_1 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
      train_data.append((text_1, {"links": dict_1}))
  
      text_2 = "Russ Cochran has been publishing comic art."
      dict_2 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
      train_data.append((text_2, {"links": dict_2}))
  
      text_3 = "Russ Cochran captured his first major title with his son as caddie."
      dict_3 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
      train_data.append((text_3, {"links": dict_3}))
  
      text_4 = "Russ Cochran was a member of University of Kentucky's golf team."
      dict_4 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
      train_data.append((text_4, {"links": dict_4}))
  
      return train_data
  
  
  # training data
  TRAIN_DATA = sample_train_data()
  
  
  @plac.annotations(
      kb_path=("Path to the knowledge base", "positional", None, Path),
      vocab_path=("Path to the vocab for the kb", "positional", None, Path),
      output_dir=("Optional output directory", "option", "o", Path),
      n_iter=("Number of training iterations", "option", "n", int),
  )
  def main(kb_path, vocab_path, output_dir=None, n_iter=50):
      """Create a blank model with the specified vocab, set up the pipeline and train the entity linker.
      The `vocab` should be the one used during creation of the KB."""
      # create blank English model with correct vocab
      nlp = spacy.blank("en")
      nlp.vocab.from_disk(vocab_path)
      nlp.vocab.vectors.name = "spacy_pretrained_vectors"
      print("Created blank 'en' model with vocab from '%s'" % vocab_path)
  
      # Add a sentencizer component. Alternatively, add a dependency parser for higher accuracy.
      nlp.add_pipe(nlp.create_pipe('sentencizer'))
  
      # Add a custom component to recognize "Russ Cochran" as an entity for the example training data.
      # Note that in a realistic application, an actual NER algorithm should be used instead.
      ruler = EntityRuler(nlp)
      patterns = [{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]}]
      ruler.add_patterns(patterns)
      nlp.add_pipe(ruler)
  
      # Create the Entity Linker component and add it to the pipeline.
      if "entity_linker" not in nlp.pipe_names:
          # use only the predicted EL score and not the prior probability (for demo purposes)
          cfg = {"incl_prior": False}
          entity_linker = nlp.create_pipe("entity_linker", cfg)
          kb = KnowledgeBase(vocab=nlp.vocab)
          kb.load_bulk(kb_path)
          print("Loaded Knowledge Base from '%s'" % kb_path)
          entity_linker.set_kb(kb)
          nlp.add_pipe(entity_linker, last=True)
  
      # Convert the texts to docs to make sure we have doc.ents set for the training examples.
      # Also ensure that the annotated examples correspond to known identifiers in the knowledge base.
      kb_ids = nlp.get_pipe("entity_linker").kb.get_entity_strings()
      TRAIN_DOCS = []
      for text, annotation in TRAIN_DATA:
          with nlp.disable_pipes("entity_linker"):
              doc = nlp(text)
          annotation_clean = annotation
          for offset, kb_id_dict in annotation["links"].items():
              new_dict = {}
              for kb_id, value in kb_id_dict.items():
                  if kb_id in kb_ids:
                      new_dict[kb_id] = value
                  else:
                      print(
                          "Removed", kb_id, "from training because it is not in the KB."
                      )
              annotation_clean["links"][offset] = new_dict
          TRAIN_DOCS.append((doc, annotation_clean))
  
      # get names of other pipes to disable them during training
      pipe_exceptions = ["entity_linker", "trf_wordpiecer", "trf_tok2vec"]
      other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
      with nlp.disable_pipes(*other_pipes):  # only train entity linker
          # reset and initialize the weights randomly
          optimizer = nlp.begin_training()
          for itn in range(n_iter):
              random.shuffle(TRAIN_DOCS)
              losses = {}
              # batch up the examples using spaCy's minibatch
              batches = minibatch(TRAIN_DOCS, size=compounding(4.0, 32.0, 1.001))
              for batch in batches:
                  texts, annotations = zip(*batch)
                  nlp.update(
                      texts,  # batch of texts
                      annotations,  # batch of annotations
                      drop=0.2,  # dropout - make it harder to memorise data
                      losses=losses,
                      sgd=optimizer,
                  )
              print(itn, "Losses", losses)
  
      # test the trained model
      _apply_model(nlp)
  
      # save model to output directory
      if output_dir is not None:
          output_dir = Path(output_dir)
          if not output_dir.exists():
              output_dir.mkdir()
          nlp.to_disk(output_dir)
          print()
          print("Saved model to", output_dir)
  
          # test the saved model
          print("Loading from", output_dir)
          nlp2 = spacy.load(output_dir)
          _apply_model(nlp2)
  
  
  def _apply_model(nlp):
      for text, annotation in TRAIN_DATA:
          # apply the entity linker which will now make predictions for the 'Russ Cochran' entities
          doc = nlp(text)
          print()
          print("Entities", [(ent.text, ent.label_, ent.kb_id_) for ent in doc.ents])
          print("Tokens", [(t.text, t.ent_type_, t.ent_kb_id_) for t in doc])
  
  
  if __name__ == "__main__":
      plac.call(main)
  
      # Expected output (can be shuffled):
  
      # Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
      # Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ("his", '', ''), ('reprints', '', ''), ('include', '', ''), ('The', '', ''), ('Complete', '', ''), ('EC', '', ''), ('Library', '', ''), ('.', '', '')]
  
      # Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
      # Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ('has', '', ''), ('been', '', ''), ('publishing', '', ''), ('comic', '', ''), ('art', '', ''), ('.', '', '')]
  
      # Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
      # Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('captured', '', ''), ('his', '', ''), ('first', '', ''), ('major', '', ''), ('title', '', ''), ('with', '', ''), ('his', '', ''), ('son', '', ''), ('as', '', ''), ('caddie', '', ''), ('.', '', '')]
  
      # Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
      # Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('was', '', ''), ('a', '', ''), ('member', '', ''), ('of', '', ''), ('University', '', ''), ('of', '', ''), ('Kentucky', '', ''), ("'s", '', ''), ('golf', '', ''), ('team', '', ''), ('.', '', '')]
  