def build_coocc_classifier_dataset(out_file, dataset_dir, entity_db_file,
**kwargs):
dataset = DatasetLoader(dataset_dir)
entity_db = EntityDB.load(entity_db_file)
ret = coocc_classifier.build_dataset(dataset, entity_db, **kwargs)
joblib.dump(ret, out_file)
def build_page_classifier_dataset(out_file, dataset_dir, page_db_file,
entity_db_file, **kwargs):
dataset = DatasetLoader(dataset_dir)
entity_db = EntityDB.load(entity_db_file)
page_db = PageDB(page_db_file, 'r')
ret = page_classifier.build_dataset(dataset, page_db, entity_db, **kwargs)
joblib.dump(ret, out_file)
def generate_corpus(dump_file, entity_db_file, out_file, abstract_db,
**kwargs):
entity_db = EntityDB.load(entity_db_file)
if abstract_db:
abstract_db = AbstractDB(abstract_db, 'r')
word2vec.generate_corpus(dump_file, entity_db, out_file, abstract_db,
**kwargs)
def build_scorer_dataset(clf_cache_file, out_file, dataset_dir, entity_db_file,
**kwargs):
clf_cache = joblib.load(clf_cache_file, mmap_mode='r')
dataset = DatasetLoader(dataset_dir)
entity_db = EntityDB.load(entity_db_file)
ret = scorer.build_dataset(dataset, clf_cache, entity_db, **kwargs)
joblib.dump(ret, out_file)
def evaluate_text_classification(model_file, entity_db_file, min_link_prob,
min_disambi_score, tagme_cache, **kwargs):
entity_db = EntityDB.load(entity_db_file)
if tagme_cache:
tagme_cache = joblib.load(tagme_cache)
entity_linker = TagmeEntityLinker(entity_db, min_link_prob,
min_disambi_score, tagme_cache)
text_classification.evaluate(model_file, entity_linker, **kwargs)
def train_model(db_file, entity_db_file, vocab_file, word2vec, **kwargs):
db = AbstractDB(db_file, 'r')
entity_db = EntityDB.load(entity_db_file)
vocab = Vocab.load(vocab_file)
if word2vec:
w2vec = ModelReader(word2vec)
else:
w2vec = None
train.train(db, entity_db, vocab, w2vec, **kwargs)
def train_model(db_file, entity_db_file, vocab_file, word2vec, **kwargs):
db = AbstractDB(db_file, 'r')
entity_db = EntityDB.load(entity_db_file)
vocab = Vocab.load(vocab_file)
if word2vec:
w2vec = ModelReader(word2vec)
else:
w2vec = None
train.train(db, entity_db, vocab, w2vec, **kwargs)
def cache_classifier_results(page_db_file, out_file, dataset_dir,
entity_db_file, init, **kwargs):
page_db = PageDB(page_db_file, 'r')
dataset = DatasetLoader(dataset_dir)
entity_db = EntityDB.load(entity_db_file)
if init:
initial_data = joblib.load(init)
else:
initial_data = {}
scorer.cache_classifier_results(dataset, initial_data, out_file, page_db,
entity_db, **kwargs)
def train_model(description_db_file, entity_db_file, word_vocab_file,
entity_vocab_file, target_entity_vocab_file, out_file,
embedding, **kwargs):
description_db = DescriptionDB(description_db_file)
entity_db = EntityDB.load(entity_db_file)
word_vocab = WordVocab.load(word_vocab_file)
entity_vocab = EntityVocab.load(entity_vocab_file)
target_entity_vocab = EntityVocab.load(target_entity_vocab_file)
embeddings = [EmbeddingReader.load(f) for f in embedding]
train.train(description_db, entity_db, word_vocab, entity_vocab,
target_entity_vocab, out_file, embeddings, **kwargs)
def evaluate_entity_typing(model_file, entity_db_file, dataset_dir, embedding,
**kwargs):
entity_db = EntityDB.load(entity_db_file)
if embedding:
model = EmbeddingReader.load(model_file)
entity_embedding = model.entity_embedding
entity_vocab = model.entity_vocab
else:
model = train.load_model(model_file)
entity_embedding = model.target_entity_embedding
entity_vocab = model.target_entity_vocab
entity_typing.evaluate(entity_embedding, entity_vocab, entity_db,
dataset_dir, **kwargs)
def build_entity_vocab(description_db_file, entity_db_file, out_file,
target_vocab, white_list, **kwargs):
description_db = DescriptionDB(description_db_file)
if target_vocab:
target_vocab = EntityVocab.load(target_vocab)
entity_db = EntityDB.load(entity_db_file)
white_titles = []
for f in white_list:
white_titles += [l.rstrip().decode('utf-8') for l in f]
entity_vocab = EntityVocab.build(description_db,
entity_db,
white_titles,
start_index=1,
target_vocab=target_vocab,
**kwargs)
entity_vocab.save(out_file)
def build_entity_db(dump_file, out_file, **kwargs):
db = EntityDB.build(dump_file, **kwargs)
db.save(out_file)
def generate_word2vec_corpus(dump_file, entity_db_file, out_file, **kwargs):
entity_db = EntityDB.load(entity_db_file)
word2vec.generate_corpus(dump_file, entity_db, out_file, **kwargs)
def build_sentence_db(wiki_sentences_file, entity_db_file, out_file):
entity_db = EntityDB.load(entity_db_file)
SentenceDB.build(wiki_sentences_file, entity_db, out_file)
def run(dataset_dir, entity_db, **kwargs):
dataset = DatasetLoader(dataset_dir)
entity_db = EntityDB.load(entity_db)
scorer.run(dataset=dataset, entity_db=entity_db, **kwargs)
def build_entity_db(dump_file, out_file, **kwargs):
db = EntityDB.build(dump_file, **kwargs)
db.save(out_file)
def build_vocab(db_file, entity_db_file, out_file, **kwargs):
db = AbstractDB(db_file, 'r')
entity_db = EntityDB.load(entity_db_file)
vocab = Vocab.build(db, entity_db, **kwargs)
vocab.save(out_file)
def build_vocab(db_file, entity_db_file, out_file, **kwargs):
db = AbstractDB(db_file, 'r')
entity_db = EntityDB.load(entity_db_file)
vocab = Vocab.build(db, entity_db, **kwargs)
vocab.save(out_file)
def build_description_db(nif_context_file, nif_text_links_file, entity_db_file,
out_file, **kwargs):
entity_db = EntityDB.load(entity_db_file)
DescriptionDB.build(nif_context_file, nif_text_links_file, entity_db,
out_file, **kwargs)
def build_page_db(dataset_dir, entity_db_file, **kwargs):
dataset = DatasetLoader(dataset_dir)
entity_db = EntityDB.load(entity_db_file)
PageDB.build(dataset, entity_db, **kwargs)
def generate_corpus(dump_file, entity_db_file, out_file, abstract_db, **kwargs):
entity_db = EntityDB.load(entity_db_file)
if abstract_db:
abstract_db = AbstractDB(abstract_db, 'r')
word2vec.generate_corpus(dump_file, entity_db, out_file, abstract_db, **kwargs)