not options.vocabf):
parser.print_help()
exit()
return options
options = _cli()
log.start(options.logfile)
log.writeConfig([
('Input embeddings', options.inputf),
('Vocabulary file', options.vocabf),
('Output embeddings', options.outputf),
('Output embeddings format', options.output_format),
])
log.startTimer('Reading node2vec embeddings from %s...' % options.inputf)
e = pyemblib.read(options.inputf,
format=pyemblib.Format.Word2Vec,
mode=pyemblib.Mode.Text)
log.stopTimer(
message='Read {0:,} embeddings in {1}s.\n'.format(len(e), '{0:.2f}'))
log.writeln('Reading vocabulary mapping from %s...' % options.vocabf)
vocab = readVocab(options.vocabf)
log.writeln('Read {0:,} vocab mappings.\n'.format(len(vocab)))
e = {vocab[int(k)]: v for (k, v) in e.items()}
log.writeln('Writing remapped embeddings to %s...' % options.outputf)
(fmt, mode) = pyemblib.CLI_Formats.parse(options.output_format)
pyemblib.write(e, options.outputf, format=fmt, mode=mode, verbose=True)
log.writeln('Done!')
options.unigrams_as_tfidf
if options.unigram_features else 'N/A'),
('Using Action oracle', options.action_oracle),
('Input predictions file', options.input_predsf),
('Pre-embedded mentions', options.pre_embedded),
]),
],
title=
"Entity linking (disambiguation) experiment using scikit-learn baseline algorithms"
)
## Data loading/setup
entity_embeds = []
for i in range(len(options.entity_embfs)):
f = options.entity_embfs[i]
t_sub = log.startTimer(
'Reading set %d of entity embeddings from %s...' % (i + 1, f))
entity_embeds.append(pyemblib.read(f, lower_keys=True))
log.stopTimer(t_sub,
message='Read %s embeddings ({0:.2f}s)\n' %
('{0:,}'.format(len(entity_embeds[-1]))))
if options.word_vocabf:
t_sub = log.startTimer('Reading word/context vocabulary from %s...' %
options.word_vocabf)
word_vocab = readVocab(options.word_vocabf)
log.stopTimer(t_sub,
message='Read %s words ({0:.2f}s)\n' %
('{0:,}'.format(len(word_vocab))))
else:
word_vocab = None
def runCrossfoldExperiment(preprocessed, preds_stream, options):
cross_fold_metrics = []
for i in range(len(preprocessed.splits)):
log.writeln(
('\n\n{0}\n Starting fold %d/%d\n{0}\n'.format('#' * 80)) %
(i + 1, len(preprocessed.splits)))
(train_ids, dev_ids, test_ids) = preprocessed.splits[i]
train, test = [], []
for _id in train_ids:
if _id in preprocessed.mentions_by_id:
train.append(preprocessed.mentions_by_id[_id])
for _id in dev_ids:
if _id in preprocessed.mentions_by_id:
if options.eval_on_dev:
test.append(preprocessed.mentions_by_id[_id])
else:
train.append(preprocessed.mentions_by_id[_id])
if not options.eval_on_dev:
for _id in test_ids:
if _id in preprocessed.mentions_by_id:
test.append(preprocessed.mentions_by_id[_id])
if options.unigram_features:
unigram_vocab = getTextVocabulary(train, preprocessed, options)
unigram_vectorizer = CountVectorizer(vocabulary=unigram_vocab,
binary=True)
else:
unigram_vectorizer = None
training_features, training_labels = [], []
for m in train:
(feature_vector,
label) = prepSample(m, preprocessed,
preprocessed.per_fold_unigram_features[i],
options)
if feature_vector is None or label is None:
continue
training_features.append(feature_vector)
training_labels.append(label)
test_features, test_labels = [], []
for m in test:
(feature_vector,
label) = prepSample(m, preprocessed,
preprocessed.per_fold_unigram_features[i],
options)
if feature_vector is None or label is None:
continue
test_features.append(feature_vector)
test_labels.append(label)
log.writeln('Number of training samples: {0:,}'.format(
len(training_labels)))
log.writeln('Number of test samples: {0:,}\n'.format(len(test_labels)))
if len(test_labels) == 0:
log.writeln(
'[WARNING] Test ids list is empty due to rounding in cross-validation splits, skipping...'
)
continue
if len(set(training_labels)) == 1:
log.writeln(
'[WARNING] Training samples for this subset have only one label class. Skipping...'
)
return None
if options.unigram_features:
training_features = scipy.sparse.vstack(training_features)
test_features = scipy.sparse.vstack(test_features)
scaler = StandardScaler(with_mean=False)
if options.normalize_features:
training_features = scaler.fit_transform(training_features)
test_features = scaler.transform(test_features)
if options.classifier == Classifier.SVM:
t = log.startTimer('Training SVM classifier...')
classifier = sklearn.svm.SVC(kernel='linear',
random_state=options.random_seed + i)
classifier.fit(training_features, training_labels)
log.stopTimer(t, message='Training complete in {0:.2f}s.\n')
t = log.startTimer('Running trained SVM on test set...')
predictions = classifier.predict(test_features)
log.stopTimer(t, message='Complete in {0:.2f}s.\n')
elif options.classifier == Classifier.KNN:
t = log.startTimer('Training k-NN classifier...')
classifier = sklearn.neighbors.KNeighborsClassifier(
n_neighbors=5,
#random_state=options.random_seed+i
)
classifier.fit(training_features, training_labels)
log.stopTimer(t, message='Training complete in {0:.2f}s.\n')
t = log.startTimer('Running trained k-NN on test set...')
predictions = classifier.predict(test_features)
log.stopTimer(t, message='Complete in {0:.2f}s.\n')
elif options.classifier == Classifier.MLP:
t = log.startTimer('Training MLP classifier...')
classifier = sklearn.neural_network.multilayer_perceptron.MLPClassifier(
max_iter=1000, random_state=options.random_seed + i)
classifier.fit(training_features, training_labels)
log.stopTimer(t, message='Training complete in {0:.2f}s.\n')
t = log.startTimer('Running trained MLP on test set...')
predictions = classifier.predict(test_features)
log.stopTimer(t, message='Complete in {0:.2f}s.\n')
metrics = SimpleNamespace()
metrics.correct = 0
metrics.total = 0
for j in range(len(predictions)):
if predictions[j] == test_labels[j]:
metrics.correct += 1
metrics.total += 1
if preds_stream:
preds_stream.write(
'Mention %d -- Pred: %d -> %s Gold: %d -> %s\n' %
(test[j].ID, predictions[j],
test[j].candidates[predictions[j]], test_labels[j],
test[j].candidates[test_labels[j]]))
metrics.accuracy = float(metrics.correct) / metrics.total
log.writeln('Fold accuracy: {0:.2f} ({1:,}/{2:,})'.format(
metrics.accuracy, metrics.correct, metrics.total))
cross_fold_metrics.append(metrics)
overall_metrics = SimpleNamespace()
overall_metrics.correct = 0
overall_metrics.total = 0
log.writeln('\n\n-- Cross-validation report --\n')
for i in range(len(cross_fold_metrics)):
m = cross_fold_metrics[i]
overall_metrics.correct += m.correct
overall_metrics.total += m.total
log.writeln(' Fold %d -- Accuracy: %f (%d/%d)' %
(i + 1, m.accuracy, m.correct, m.total))
overall_metrics.accuracy = np.mean(
[m.accuracy for m in cross_fold_metrics])
log.writeln('\nOverall cross-validation accuracy: %f' %
overall_metrics.accuracy)
return overall_metrics
('Extraction mode', config['ExtractionMode']),
('Annotation directories', config['DataDirectories']),
]
if config['ExtractionMode'] == 'csv':
settings.extend([
('Plaintext directory', config['PlaintextDirectory']),
('CSV file ID pattern', config['CSVIdentifierPattern']),
('Plaintext file render pattern', config['PlaintextIdentifierPattern'])
])
settings.extend([
('Output mentions file', options.outputf),
('Mention map file (automatic)', options.mention_map_file),
])
log.writeConfig(settings, title='Mention extraction for action classification')
t_sub = log.startTimer('Generating %s features.' % options.dataset)
mentions, mention_map = getAllMentions(config, options,
tokenizer=options.tokenizer, bert_vocab_file=options.bert_vocab_file,
log=log)
log.stopTimer(t_sub, 'Extracted {0:,} samples.'.format(len(mentions)))
log.writeln('Writing mention map information to %s...' % options.mention_map_file)
with open(options.mention_map_file, 'w') as stream:
for (mention_ID, mention_info) in mention_map.items():
stream.write('%d\t%s\n' % (mention_ID, mention_info))
log.writeln('Wrote info for {0:,} mentions.\n'.format(len(mention_map)))
t_sub = log.startTimer('Writing samples to %s...' % options.outputf, newline=False)
mention_file.write(mentions, options.outputf)
log.stopTimer(t_sub, message='Done ({0:.2f}s).')
parser.error('Must supply --definitions')
(mentionf, ) = args
return mentionf, options
## Getting configuration settings
mentionf, options = _cli()
log.start(logfile=options.logfile)
log.writeConfig([
('Mention file', mentionf),
('Entity definitions file', options.definitions_file),
('Restricting to main definitions only', options.main_only),
],
title="Adapted Lesk similarity baseline")
t_sub = log.startTimer('Reading mentions from %s...' % mentionf)
mentions = mention_file.read(mentionf)
log.stopTimer(t_sub,
message='Read %s mentions ({0:.2f}s)\n' %
('{0:,}'.format(len(mentions))))
log.writeln('Reading definitions from %s...' % options.definitions_file)
definitions = readCodeDefinitions(options.definitions_file,
options.main_only)
log.writeln('Read definitions for {0:,} codes.\n'.format(len(definitions)))
if options.preds_file:
preds_stream = open(options.preds_file, 'w')
else:
preds_stream = None
options.output_dir = os.path.dirname(options.input_f)
return options
sys.setrecursionlimit(1800)
options = args = _cli()
log.start(options.logfile)
log.writeConfig([
('Terminology file', options.input_f),
('Storing pickled maps to', options.output_dir),
('Map concepts separated by', options.sep),
('Removing stopword terms', options.remove_stopwords),
('Tokenization settings', tokenization.CLI.logOptions(options)),
], 'JET -- STR -> CUI file preprocessing')
t_sub = log.startTimer('Initializing tokenizer...')
tokenizer = tokenization.CLI.initializeTokenizer(options)
log.stopTimer(t_sub, message='Tokenizer ready in {0:.2f}s.\n')
t_sub = log.startTimer('Reading terminology file...')
ngrams, entities_by_term = readTerminology(
options.input_f,
tokenizer,
remove_stopwords=options.remove_stopwords,
use_collapsed_string=options.use_collapsed_string)
log.stopTimer(t_sub, message='Completed in {0:.2f}s.\n')
if options.verbose:
log.writeln('\nRead map:')
NGramMapPrinter.prn(ngrams)
options = _cli()
log.start(options.logfile)
log.writeConfig([
('Plaintext corpus file', options.input_f),
('Pickled ngram->term map', options.terminology_pkl_f),
('Output annotations file', options.output_f),
('Tagging settings', [
('Number of tagging threads', options.threads),
('Line queue size cap',
'unlimited' if options.maxlines <= 0 else options.maxlines),
]),
('Tokenization settings', tokenization.CLI.logOptions(options)),
], 'JET -- Automated corpus tagging')
t_sub = log.startTimer('Loading pickled strings map...')
compiled_terminology = pickleio.read(options.terminology_pkl_f)
log.stopTimer(t_sub, message='Done in {0:.2f}s.\n')
t_sub = log.startTimer('Initializing tokenizer...')
tokenizer = tokenization.CLI.initializeTokenizer(options)
log.stopTimer(t_sub, message='Tokenizer ready in {0:.2f}s.\n')
t_sub = log.startTimer('Tagging corpus...')
tagCorpus(
options.input_f,
compiled_terminology,
options.output_f,
tokenizer,
options.threads,
max_lines_in_queue=options.maxlines,
return embf, options
embf, options = _cli()
log.start(options.logfile)
log.writeConfig([
('Input embedding file', embf),
('Input embedding file mode', options.embedding_mode),
('Output neighbor file', options.outputf),
('Ordered vocabulary file', options.vocabf),
('Number of nearest neighbors', options.k),
('Batch size', options.batch_size),
('Number of threads', options.threads),
('Partial nearest neighbors file for resuming', options.partial_neighbors_file),
], 'k Nearest Neighbor calculation with cosine similarity')
t_sub = log.startTimer('Reading embeddings from %s...' % embf)
emb = pyemblib.read(embf, mode=options.embedding_mode, errors='replace')
log.stopTimer(t_sub, message='Read {0:,} embeddings in {1}s.\n'.format(len(emb), '{0:.2f}'))
if not os.path.isfile(options.vocabf):
log.writeln('Writing node ID <-> vocab map to %s...\n' % options.vocabf)
writeNodeMap(emb, options.vocabf)
else:
log.writeln('Reading node ID <-> vocab map from %s...\n' % options.vocabf)
node_map = readNodeMap(options.vocabf)
# get the vocabulary in node ID order, and map index in emb_arr
# to node IDs
node_IDs = list(node_map.keys())
node_IDs.sort()
ordered_vocab = [